The findings, which were collected from 35,000 children across 28 districts in 26 Indian states, were based on these four domains. Additionally, the study included a series of qualitative interviews conducted in Sitapur (Uttar Pradesh), Solan (Himachal Pradesh), and Dhamtari (Chhattisgarh), which delved deeper into the aspirations of the youth.

Education doesn’t automatically translate into increased ability

The report shows that overall, approximately 87 percent of the surveyed youth are enrolled in some kind of educational institution. The ratio of school and college dropouts has reduced over the years, with more young people completing senior secondary school than ever before. But when those who are not enrolled were asked why they discontinued their studies, the most commonly cited reason at 18.9 percent was ‘lack of interest’. Financial concerns, family constraints, failing to pass exams, and other challenges fell lower on the scale. Interestingly, 26 percent of those not enrolled in any educational institution reported that they used their smartphones regularly for some educational activity, such as watching online videos, exchanging notes, and resolving doubts.

“We can learn how to manage a household, how to talk to others, how to present ourselves, and how to respect the people around us,” a girl in class 10 from Sitapur responded when asked about the benefits of education.

For an age group that is expected to learn trigonometry and calculus as per the curriculum, only 43 percent could solve basic division problems.

However, it appears that years of schooling do not necessarily translate into proportional levels of learning. Approximately 75 percent could read a class 2 level text in their regional language and 57 percent could read simple texts in English. For an age group that is expected to learn trigonometry and calculus as per the curriculum, only 43 percent could solve basic division problems, and 10 percent could calculate simple interest on loans.

The current system doesn’t appear to account for the socio-economic background of a learner. Many of the youngsters reported that they have to work while pursuing education. Approximately 77 percent of the youth surveyed do some form of household work. Of the 34 percent who reported engaging in some form of paid work for more than 15 days in a month, 85 percent participate in agricultural work. The education system doesn’t reward children who work—rather, there’s a higher probability that their performance at school declines due to the increased workload that comes in secondary school.

Most of these findings align with those observed in ASER 2017. However, since digital access was increasingly incorporated in education during and post the pandemic, ASER 2023 added a digital skills assessment component, wherein youth were asked to attempt basic tasks using a smartphone. The test offers a clearer insight into the way technology has fundamentally changed the way youth are learning and thinking about their future.

Digital literacy is not the problem

Over the past few years, there has been a steady increase in smartphone penetration in India. True to this trend, ASER 2023 observed that 90 percent of the adolescents surveyed had access to a smartphone and 67.1 percent of the total sample were able to produce a smartphone during the survey itself (others indicated that the smartphone was with a parent/sibling who wasn’t present at home during the survey). Approximately 92 percent of those who were able to produce a smartphone during the survey were able to complete the digital tasks successfully on the following aspects.  

1. Ease of usability

Approximately 80 percent of young people who possess smartphones are capable of locating a particular video on YouTube, and of this group, 90 percent know how to share it with a friend. In addition, 70 percent can navigate the internet to seek answers to inquiries, while close to two-thirds are able to set an alarm for specific times. A little more than one-third can use Google Maps to ascertain the duration of travel between two destinations.

During these digital assessments, boys tended to perform better than girls in most tasks. However, the primary factor in this case appeared to be smartphone ownership. Among those who knew how to use smartphones, 43.7 percent boys and 19.8 percent girls actually owned the smartphone. When the differences in ownership disappear, so does the difference in digital skills between boys and girls. Computer ownership was found to be far lower than smartphones (less than 10 percent), but there were similar trends in digital skill patterns. Digital literacy is directly proportional to increased access to devices.

2. Tech for creativity and life skills

The survey clearly indicated that young people look at technology as a pathway for creative expression. Approximately 78 percent use their smartphones for entertainment-related activities such as watching movies or listening to music, 57 percent play games regularly, and 90 percent had used some form of social media in the previous week.

“With a phone in our hands, we can learn anything without having to spend money or ask anyone [for permission],” Shristhi Sandhil, an 18-year-old from Jharkhand, said as she talked about how she uses YouTube for learning new creative skills. This sentiment was echoed by many, who highlighted that it was the freedom afforded by the smartphone that made them turn to it.

The smartphone has enabled wider skill acquisition by cutting across barriers of access and opportunity. More than 35 percent of the youth reported using their smartphone to engage in dance, music, photography, and other hobbies.

Given the increased use of technology, including for creative expression, it is important to acknowledge that what was once an extra-curricular might have to become a part of the core of the curriculum. While creativity has a significant place in the cultural context of Indian communities and despite its recognition as a key twenty-first century skill, it is yet to be brought to life in a tangible manner by the education system. Through games, projects, activities, and other forms of interactive learning approaches enabled by technology, creativity needs to become a core facet of academic learning.

Young peoples’ aspirations tell a story

When asked about their aspirations, there were clear patterns across states and genders. For instance, in terms of career aspirations, while ‘nursing’ was the top voted choice in Kerala for girls, it was ‘teaching’ in Rajasthan, ‘doctor’ in Jammu and Kashmir, and ‘police’ in Maharashtra. Similarly, boys in Assam chose ‘army’, those in Tamil Nadu chose ‘engineering’, and in Chhattisgarh it was ‘agriculture’. While girls indicate a stronger aspiration for higher education, boys appeared to prioritise income generation as they plan their careers.

A boy studying in class 10 in Dhamtari told us, “I will become famous and gain respect in the community—there’s a boy from the village went into the army. My father had failed high school, but because of this [his son joining the army] he will also gain recognition. I will get money as well. And I will be able to protect the country.”

Both boys and girls indicated that social responsibilities would ultimately shape their decisions.

But what was concerning is that approximately 1 in 5 youth surveyed said that they did not know what they wished to pursue. Of the ones who indicated preference for a particular kind of work, 45 percent indicated that they didn’t know anyone engaged in that line of work. While more than 40 percent reported using a smartphone to search for information related to their future career, the availability of a role model in their community appears to play a strong role in determining if youth are able to make choices related to their career.

Both boys and girls also indicated how social responsibilities would ultimately shape their decisions. A class 10 girl from Sitapur said to the survey team, “My father says he will let me complete my BA  before I am to be married, although my brother says that they can arrange my married once I get admitted. I can’t say anything in such matters; it is up to them.” This experience was similar for many girls, who indicated that marriage will play a strong role in determining their future, while boys felt they were expected to earn enough to pay for all household expenses. These reasons also lead to a lack of aspirations around vocational work and agriculture, as they are not seen as socially acceptable or lucrative futures.

The influence of socio-economic contexts on career choices is undeniable. This underscores the need to establish a system that recognises the role that social milestones play in determining the career choices of young people. At the same time, the education system should provide pathways that enable youth to overcome the barriers imposed by these conventions.

What next?

Rather than inspiring lifelong learning, it appears that the consequence of the current model is burnout before adulthood. When the education system acts as sieve, and we see masses of youth who are unskilled and unemployable, we must ask ourselves: Why are we still trying to filter our adolescents based on their ability to clear exams that might not be relevant for the current job market? There are some clear learnings from ASER 2023 that educators need to apply:

• As access to smart devices increases, it is probable that digital literacy will automatically grow. This needs to be leveraged to help youth acquire skills that are going to be relevant for the future of work.
• Increased access to smartphones offers us the opportunity to develop open learning models that includes focus on twenty-first century skills like creativity without worrying about the restrictions typically faced by the school system.
• Young people need accessible role models in order to be able to break social conventions and make meaningful decisions to pursue their dreams.

We need to shift towards a model that incentivises learners to learn more, well after they leave the confines of a formal system.

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Know more

• Read this article to learn potential ways of solving India’s digital divide.
• Learn more about empowering the youth to build sustainable futures.
• Read this article to learn why reliable data on learning outcomes is crucial.

Rekha Koita is the director and co-founder of Koita Foundation, an organisation that works on digitally transforming nonprofit operations to optimise processes and use data and analytics to drive performance and growth.

She was previously a management consultant at Accenture where she conducted corporate training for several Indian and multinational organisations and nonprofits. Since starting her philanthropic journey in 2016, Rekha has been focused on leveraging technology for positive change within the nonprofit sector.

In this interview, Rekha Koita reflects on the potential of the current philanthropic landscape in India and her role in it as a donor. She also talks about visionary founders, many of whom are young and driven, spearheading initiatives that promise to reshape communities for the better.

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Know More

• Read this article about how Indian philanthropies need to fill the funding gap.
• Read this article about Prashanth Prakash’s philanthropic journey.

While it is important to focus on measures to reduce GHG emissions from the sector, it is equally critical to ensure that farmer livelihoods and agricultural productivity don’t get impacted. In light of this, there has been a growing focus on the practice of regenerative agriculture. This shift aims to encourage more sustainable farming methods among farmers without imposing undue financial burdens on them. Regenerative agriculture places emphasis on restoring soil health in order to decrease GHG emissions. It involves minimising soil disturbance through practices such as reduced tillage or no-till farming, fostering biodiversity through cover cropping and crop rotation, and integrating trees or shrubs into agricultural landscapes through agroforestry. 

However, in order to make tangible progress in this fight, reliable and accessible data is crucial for organisations that work in close conjunction with such small and economically disadvantaged farmers. These include nonprofits, research institutions, and governmental agencies working on sustainable farming practices, rural development, and improving the livelihoods of small-scale farmers. Such organisations need access to granular data, updated in real time, in order to facilitate research and formulate a strategic implementation plan for each farm. This involves the collection of essential information, including details on farmers, average landholdings, water resource availability, saplings planted, and the survival rates of these plants.

But technology is expensive. Small nonprofits that work with farmers at the grassroots often find it difficult to spend on hardware, software, and data collection tools. Their inability to adopt innovation not only affects their efficacy to implement programmes on ground but also makes their organisational functioning time-consuming. So how can technology be made more accessible and how can its benefits be extended to a wide array of nonprofits working on these issues?

The promise of free and open-source software

One answer may lie in utilising free and open-source software (FOSS) that is already available in the market. FOSS allows users to access, modify, and share data easily. It is affordable and highly customisable, also allowing for collaboration between different stakeholders. In stark contrast, proprietary or vendor systems often lack transparency on how the system operates and their pricing structures can be exorbitant, with additional charges for every new feature or product recommendation.

At Tech4Good Community (T4GC), we have been actively advancing the potential of FOSS with several nonprofits that may be constrained by resources and the capacity to incorporate technology into their operations. Agro Rangers (AR)—a grassroots nonprofit organisation working on adoption of regenerative agricultural practices with farmers in Pune, Maharashtra—is one of our partners. They work with agricultural communities on ground, focusing on creating agroforestry interventions, improving soil quality, and transitioning from chemical to organic farming practices. This requires meticulous collection of data, including information on soil composition, carbon levels, and climatic conditions encompassing temperature, water availability, and humidity of each farm in each location. Additionally, there is a need to track organisational data including employee leave and expenses.

Here we share learnings from our work with AR and how FOSS helped make the process of data collection easier for its teams.

1. Enabled smarter data collection

In the case of AR, a field manager oversees the operations of 150 farmers. The current implementation covers 50 farmers, with plans for expansion. As new farmers are brought into the system, specific data is gathered during the distribution of saplings based on individual preferences. For instance, some farmers may request a combination of fruit saplings like mango and lemon, while others prefer alternative varieties such as jamun and custard apple. The field manager ensures precise sapling distribution and simultaneously records essential details. This data, including instances of surplus saplings that require later retrieval, forms a secondary layer of information guiding future distribution strategies. Throughout the year, the team diligently monitors sapling survival rates, offering comprehensive insights into inventory management and the overall effectiveness of their agricultural initiatives.

However, manually collecting and organising this data was proving to be a labour-intensive exercise, with the added complexity of managing multiple forms concurrently. When AR was using Google Forms, which at times was the case, they couldn’t progress to collect data from the next farmer until the previous one had submitted their form.

The introduction to KoboToolbox streamlined AR’s laborious data collection process. Kobo is a FOSS technology that collects, analyses, and manages data for surveys, monitoring, evaluation, and research. It allowed AR to customise its data collection forms to include a variety of question types. Once the forms were designed, they were deployed to mobile devices through the KoboCollect app, facilitating offline data collection where an internet connection is unavailable.

The system aids in tracking sapling distribution, identifying inventory needs, and streamlining the planting process.

Kobo also allowed AR to pause the data entry for one farmer and seamlessly move on to the next person, eliminating the waiting period for form submission, which ranged anywhere from five hours to two days. There were many other advantages too. The software not only streamlined data collection but also facilitated user identification, ensuring that each farmer’s progress and unique needs could be effectively monitored and addressed. The software also helped capture data related to labour deployed on each farm, which was previously documented in cumbersome registers. This meant that AR now had access to extensive data on the gender ratio of employment in every small landholding, as modest as one acre.

The collected data can be securely stored on the KoboToolbox server, organised and managed through the web interface, which provides features for data analysis and visualisation. This now enables AR to monitor progress, allocate resources efficiently, and adjust strategies based on real-time data. For instance, the system aids in tracking sapling distribution, identifying inventory needs, and streamlining the planting process. This capability allows for prompt adjustments and informed decision-making in the face of climatic variations, enhancing the overall effectiveness of climate-focused initiatives.

2. Streamlined internal data management

For internal operations, AR relied heavily on manual processes. Donor records, volunteer management, and team leave dates were all managed on Excel. This meant that for the founder of the organisation, with a small team and no dedicated staff, the sheer volume of data and the responsibility of keeping track of reporting structures and finances became incredibly time-consuming. This diverted focus from programmatic work, highlighting a significant organisational challenge. The absence of dedicated personnel and sophisticated software further compounded the need for a more productive system.

Once implemented, ERP systems act as centralised databases, consolidating data from different departments into a unified platform.

The introduction of an enterprise resource planning (ERP) system helped substantially streamline these operations. ERP refers to a type of software that organisations use to manage day-to-day business activities such as accounting, procurement, project management, risk management and compliance, and supply chain operations. A complete ERP suite also includes enterprise performance management, a software that helps plan, budget, predict, and report on an organisation’s financial results. Once implemented, ERP systems act as centralised databases, consolidating data from different departments into a unified platform. This integration facilitates real-time communication and collaboration across various business functions.

For AR, ERP has enabled real-time tracking of team members’ in-and-out times, leave, and salaries. It has also helped in managing volunteers. Additionally, this integration has assisted AR in donor management; utilisation of funds can now be tracked in detail, highlighting the areas where expenses were incurred. Features such as automated reminders for pending installments, generation of 80G receipts, and handling of income-tax-related forms like the 10B form have also been incorporated.

3. Made data shareable and easy to understand

To enable easier analysis of all the data collected through KoboToolbox, Metabase was integrated into AR’s processes. Metabase is an open-source business intelligence tool that enables organisations to study data from a variety of destinations and sources, lets them ask questions about their data, and displays answers in user-friendly formats such as bar charts or a detailed table.

This integration empowers AR employees, regardless of technical expertise, to shift their focus from sorting through disparate data formats to concentrating on operational tasks. With Metabase, AR can make informed decisions. For instance, it now has the potential to facilitate the analysis of various farming models to identify successful approaches as well as those in need of modifications. Furthermore, it can provide the team with valuable insights into the types of saplings favoured by farmers, enabling them to tailor their initiatives to better align with farmer choices and needs. Metabase also allows AR to track the impact their projects had and clearly communicate their results to donors, customers, and corporate and foundation partners, as well as to new partners.   

With regenerative agriculture emerging as a crucial lever in addressing climate challenges, it becomes imperative for grassroots nonprofits working in this domain to leverage technology that suits their unique needs. The cost-effectiveness of FOSS is a compelling reason for nonprofits to explore and adopt these solutions, ensuring they maximise their impact while minimising financial burden. However, several challenges remain. Beyond technical and financial constraints, some organisations may be hesitant to depart from traditional manual processes and proprietary software. Data privacy and security concerns are also relevant for nonprofits that handle sensitive information, and these must be taken into consideration. Nevertheless, the escalating challenges posed by the climate crisis underscore the importance for data-driven climate advocacy.

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Know more:

• Read this to learn more about the importance of quality data to drive sustainable farming practices.
• Read this article to learn more about the benefits of open-source technology for nonprofits.

When executed strategically, the digitisation of core processes is the first step towards a nonprofit’s digital transformation. For nonprofits, this can improve not only operations but also programme outcomes. Digital transformation has a significant impact on organisational culture as well: It can make the nonprofit more data-driven, fostering greater transparency, accountability, and numerical integrity. Since donors view digitisation in a positive light, it’s also advantageous when trying to raise money.

While the benefits of implementing the right digital tools are immense, the path to transformation is not easy. Koita Foundation has worked with more than 20 nonprofits on building tech solutions, such as digitising front-end field operations and building management applications as well as business analytics platforms.

Based on our learnings, here are six essential factors for a successful digital transformation.

1. The CEO should be committed to digital transformation

For technology to be effectively deployed in a nonprofit, the CEO must have a vision for the organisation’s digital transformation. This is important for motivating the entire team, particularly the programme personnel, and ensures that everyone works towards the same goals.

In addition, the CEO’s ongoing and visible commitment is essential—there is no substitute for this. This involves being part of key decisions during the design phase and driving or actively reviewing the initiative during the implementation phase and beyond. Most technology projects fail when teams don’t comply with new business processes—a problem that only the CEO can address.

One important thing we have learned is that the CEO should insist on reviewing only system-generated data and reports after the new processes are live. This is only possible when all the team members use the system in the right way.

Uma Kogekar, CEO of CEQUE, took this approach during the organisation’s digitisation journey. Besides reviewing only system-generated reports, Uma would call on team members to do a show-and-tell—this got them to reflect on what the data was showing them, and what insights they could gain from it. It also created a higher degree of awareness among the team members and ensured that they used the system effectively.

2. Identify the right areas for the first phase of the transformation

Digital transformation is a multi-year, multi-step journey. Therefore, picking the right area for executing the first phase is critical to achieving the desired results and creating momentum for the subsequent phases of transformation. The first phase of digitisation should be big enough to yield tangible results, but not so large that it takes too long to deliver any results.

Based on our learnings, we believe that this initial stage should focus on digitising ‘front-end’ processes—that is, those involving frontline workers and the communities being targeted—in areas where technology can have a visible and tangible impact. Also, this phase should be scoped such that the results are delivered within six to nine months.  

Antarang Foundation’s CareerAware programme is a case in point. Antarang conducts psychometric testing with students from grades 10 and 12 to help them figure out a suitable career path based on their aptitude and aspirations. Since the entire process was manual, they were able to conduct this exercise with only 3,000 students per year.

By using technology for collecting student information and developing an algorithm for generating career recommendations and reports, Antarang was able to scale its programme by 10X to serve more than 30,000 young people in just over a year.

3. Refine existing processes before digitisation

Organisations often have certain inefficient processes that evolve and become embedded over time. It is important to identify and revamp these processes before building new tools. For example, in one of the nonprofits we worked with, the field team would scan handwritten forms and send them to the head office for data entry on a monthly basis. While the new system was being developed, this process was reworked such that data uploads started happening daily or weekly. This change required working with the field team and changing the approach to data even before the new system was ready.

Similarly, we worked with Vipla Foundation on introducing a new scoring mechanism for evaluating the performance of students enrolled in balwadis. This new mechanism also helps the Vipla team monitor the success of their programmes.

Streamlining processes is, therefore, as much about simplifying existing methods as it is about introducing new, tech-enabled ones. Without this step, you would just be digitising a clunky process, which in turn could render the digital process inefficient.

4. Actively involve end users in the design and development phase

New technology products usually face significant adoption issues when they do not adequately meet user requirements. Often this inadequacy is due to a lack of engagement with end users during the design phase. So, it is essential for the team to articulate their technology-related requirements clearly. Since end users closely experience these issues, their feedback should be incorporated into the design during the initial stages. In addition, the project team should ensure that mock-up screens of the application are shared with and vetted by the end users. This approach not only enables the development of a better application, but also creates positive expectations and excitement in end users even before the application is built.

5. Build ownership across the organisation

Putting together a strong cross-functional team is crucial for digital transformation. The team should be headed by a strong leader who has a deep understanding of business and operations and is forward-thinking. Other members of the core team should be drawn from other parts of the organisation to drive different aspects of the project: getting the requirements right, end-to-end design, interacting with vendors, and implementation and user support.

6. Plan for a phased implementation and post-implementation support

Once your tech solution has been developed and tested, it is time to roll it out. A detailed implementation plan—one that factors in the needs of different user groups working across programmes and geographies—is important at this stage. The implementation plan should include user training, data migration, procurement and setting up of relevant hardware, and so on.  

Post-implementation engagement through regular Zoom calls or WhatsApp groups is also necessary and should include both technology team members and end users. This enables the former to learn about and resolve end-user issues in a direct and timely manner.

Another best practice is tracking the usage of apps and giving due recognition to team members who are driving initial usage. At Vipla Foundation, for instance, team members who were using the apps the most enthusiastically were rewarded in a felicitation ceremony that celebrated the collective success of all users. Identifying ‘strong’ users who can champion the app or digital product is also a good practice—these ‘champions’ can spread the message among their peers and boost adoption.

Digital transformation is a vital strategic lever for driving performance and scale and should be a priority for the CEO and leadership team. Solutions that are thoughtfully designed, developed, and implemented can create genuine, long-term impact for a nonprofit.

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Know more

• Read this article to learn about a step-by-step approach to building your nonprofit’s tech capacity.
• Read this article to learn more about the role of nonprofit leaders in an organisation’s digital transformation.

In the last year or so, a subset of AI—generative AI—has been gaining traction. Generative AI uses deep learning to analyse existing sets of data to create new outputs. Unlike its predecessors, generative AI also has reasoning capabilities. ChatGPT, which can produce human-like responses to text prompts, and DALL-E, which can create images and artworks from text prompts, are popular examples of generative AI. 

The rise of generative AI has raised curiosity and piqued interests. It’s early days and there’s no clear verdict, but its potential has opened up many possibilities. This article looks at some of these possibilities and highlights how generative AI can be effectively adopted in the education sector.

Generative AI in education

Generative AI can help bridge many gaps in a country like India that has vast cultural and social differences and barriers of inequality. It can be beneficial to various sets of stakeholders in the education system, be it students, teachers, or parents.

Recognising the importance of developing AI skills for children, CBSE has introduced AI as a skill module in classes 6–8 and as a skill subject in classes 9–12. Additionally, there are several organisations that are creating virtual assistants for students, teachers, and parents to enable them to learn and teach better. Many such initiatives are now being seen across a diverse set of use cases.

Here are some potential ways in which generative AI can be used:

1. Parents can leverage virtual assistants to figure out activities they can do with their child to help enhance their reading and comprehension skills. For example, parents can narrate stories generated by the AI to the child or get the child to read aloud a story. This can be especially useful for parents who aren’t literate but want to be involved in their child’s education.

2. Generative AI can help teachers follow the prescribed guidelines for teaching in an efficient way without spending hours going through multiple reading materials. A virtual assistant built on generative AI can assist a teacher in planning unique and engaging classroom activities by referring to a selection of carefully chosen documents and expert insights and suggesting methods that may work in a class setting.

3. This technology can adapt to the unique needs of a child and so, under the assistance of a caring adult (teacher, parent, or community member), it can be extremely helpful in early childhood education where learning pace and approaches vary from one child to another. It can be useful in developing foundational literacy and numeracy and teaching basic language skills.

4. Generative AI can assist with speech-to-text, text-to-speech, and speech-to-speech translations, and also adjust the tone and cultural context while translating. This will help in making education more inclusive for children from various linguistic and sociocultural backgrounds. 

5. Generative AI can help create virtual labs on smartphones, especially for students in senior grades and colleges. This will be particularly useful for students from marginalised backgrounds who may not have access to a physical lab to perform science experiments or learn vocational skills. AI can aid in helping them understand these skills and concepts.

6. Virtual assistants can be used to resolve students’ doubts and queries and also help them in developing skills such as critical thinking, creativity, problem-solving, and communication. It can be a function of how one trains the virtual assistant to aid students in developing these skills. Students can speak with the virtual assistant in their local language, write and scan text, or type into it directly. Similarly, a school app that uses virtual assistants can be customised for students, teachers, and parents to track assignments, attendance, results, etc.

Many of these are already being piloted by various organisations that are working closely with a set of stakeholders on the ground.

Approaches for effective adoption

While there are many possible ways to implement it, currently the adoption of generative AI in the education space remains minimal and experimental. In order to incorporate it in our programmes more effectively, the following things need to be kept in mind:

1. Picking the right problem

Amid the myriad of potential applications for generative AI, it’s crucial to ensure that our focus is directed towards solving and addressing the correct issues. For example, it is important to identify the real problems that a teacher faces while teaching a class or managing it. Can there be more constructive methods of doing so? What’s the most practical way for a teacher to do this without giving up their own agency? Are the suggestions contextual and relevant for the teacher to implement?

Forums and events, user research, and focus group discussions with the community serve as a valuable compass for shaping appropriate solutions and policies.

One useful approach involves gaining insights into the challenges faced by the target audience and relevant stakeholders. Forums and events, user research and understanding, and focus group discussions that engage with the community and delve into their real-world experiences serve as a valuable compass for shaping both appropriate solutions as well as policies. These foster open communication and deeper empathy towards the problem so that solutions and policies are co-created while paying attention to inclusion and co-designing for diverse stakeholders and perspectives.

In the early stages, it is also useful to ensure that programmes are undertaken in a low-stakes manner. This means that evolution goes through stages with appropriate checks and balances. To avoid undesirable results, one can always figure out the value proposition at a smaller scale. 

2. Building the ecosystem

Most organisations working in the education space are not equipped to build or leverage generative AI. For example, if an organisation wants to build a storytelling application that narrates stories to children in the local dialect, they would need a diverse set of experts from the technology ecosystem, besides domain experts. This calls for greater synergy between the education space and the tech space and a conducive ecosystem that supports them. However, fostering such collaborations requires resources, money, permissions, and access. Therefore, without the support of funders, policymakers, and a larger ecosystem, this synergy is not possible.

3. Figuring out who takes responsibility

When an organisation engages technology experts to develop applications on their behalf, the issue of responsibility and ownership becomes quite complicated. For instance, if an organisation collaborates with a state government and a technology expert to implement a new application, the following questions arise: Who assumes the charge of managing and monitoring the app? Who rightfully claims ownership of the technology and shoulders the onus of data governance? Who accepts accountability for any setbacks? This requires careful crafting of policy guardrails and guidelines, governance structures, and clarity in the roles and responsibilities of those involved so that stakeholder interests can be safeguarded. There needs to be a clear policy on how the data is coming in, whether it is anonymised, and what this data is being used for.

4. Checking for bias

Since generative AI is heavily dependent on the data it is being fed, biases are a pertinent threat. These biases can be detrimental to a child’s education or can lead to exclusion. While building models, it is important to ensure that no inherent bias is getting introduced and that the impact of the tool on the child has been evaluated. The onus should also extend to other players in the space including parents, teachers, and the community. For this, models should be tested thoroughly with smaller cohorts to identify and address such biases before deploying them at scale.

So far, generative AI has elicited a range of responses—either it is being hailed as the biggest technological breakthrough of the century or being met with vehement criticism. However, for wider adoption, its potential benefits need to be recognised while also acknowledging the challenges it presents.

Generative AI can help in overcoming the challenges to education in India only if it has an ecosystem that innovates and supports its growth, participates in its evaluation, and assumes responsibility for failures. This ecosystem should comprise community members; social, private, and public sector organisations; experts; thought leaders; and funders, and demand active participation from each of these stakeholders.

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Know more

• Read this article to learn about how to EdTech can work in low-resource settings.
• Read this article to learn more about generative AI in the education sector.
• Read this article to learn about the digital divide in the education sector.

Adopting new tech tools is exciting and can benefit an organisation’s work. But there are also many reasons to proceed with caution. 

One tool in particular that we’ve seen growing uptake of is the chatbot, especially in the humanitarian sector. So far, however, there has been limited research exploring risks, harms and opportunities related to their use. 

Last year we kicked off a project examining chatbot use in humanitarian work, with support from the IFRC and UNHCR. We looked into the types of chatbots used by humanitarian and civil society organisations across various contexts in more than 10 countries, including Ukraine, Ecuador, Kazakhstan and Libya. In this post, we share some of the key learnings for humanitarian organisations that emerged from our research—keep an eye out for the full report soon! 

1. Problem first, solution second, tech third

At The Engine Room, we generally find that new tech tools are best approached in the following order: problem first, solution second, tech third.

In our research, we found that sometimes organisations wanted to use a chatbot for the sake of using a chatbot (i.e. a “solution-first” approach), and not because it was necessarily the best fit for addressing the problem they needed to solve—for example, addressing specific community needs or making up for gaps in staff capacity. 

This could lead to the chatbot being unsuccessful: In cases we encountered, community needs were sometimes better addressed through other means, such as hiring more staff to conduct site visits or answer queries, while the chatbots saw low usage or poor user feedback, or just didn’t answer the questions that people were actually coming to it with (in one case, for example, a Covid misinformation bot was used by people to try and access food aid).    

In terms of solving staff capacity issues, chatbots could sometimes instead create unforeseen double work. In some cases, for example, staff ended up having to manually transfer data from the chatbot into excel spreadsheets, or scan chatbot interactions to answer unaddressed questions.

2. Centring contextual considerations

Our research found that contextual considerations tend to play a key role in whether a chatbot helps an organisation achieve its goals or not. For example, a chatbot might not be a viable option in situations where smartphone saturation is low or where people share SIM cards. Likewise, some of our interviewees mentioned that younger community members are more likely to use a chatbot integrated into a platform they use regularly, like Telegram or Whatsapp, whereas older members of the community might prefer in-person interactions. 

Other factors to take into account included things like tech literacy, access to devices and the internet, and accessibility.

3. Checking operational and user expectations 

A key research finding was the need to adjust expectations of what humanitarian practitioners want a chatbot to do, versus what the chatbots they are considering using can actually do. This means adjusting monetary and staffing expectations, as well as critically thinking about the type of chatbot (e.g. simple, FAQ or button based bots, mid-range bots, or AI-driven bots) that would be useful, if it is determined that a chatbot could be a useful tool in the first place. 

In addition to managing operational considerations, our research found that managing user expectations is essential in order to mitigate frustrating user experiences. For humanitarian organisations using chatbots, this means being transparent and upfront to affected communities about what their chatbot cando, and what it can’t. 

For example, our research saw that many people desired a level of personalisation from chatbots that most chatbots currently deployed by humanitarian organisations cannot provide. This can result in a frustrating situation when users are sent on error loops, are repeatedly shown a predetermined script or can only ask questions rather than make comments. This frustration is worsened if it is not made clear that users are interacting with a bot and not a human. 

4. Weaving in responsible data 

Throughout this project we set out to understand what responsible data means when it comes to the use of chatbots in humanitarian contexts. Responsible data considerations that came up in our interviews and desk research touched on ongoing discussions around organisational data policies, GDPR compliance, and data sharing practices and agreements between humanitarian organisations, governments and corporations (among others). 

Though responsible data is not a prescriptive practice, the following questions could be a starting point for those considering deploying chatbots in humanitarian contexts: 

• What data is collected and how is it stored (and for how long)? Is this data shared? 
• How is consent achieved? Are there viable alternatives for impacted communities to access services without using the chatbot? 
• What data protection agreements are in place? Are risk assessments being conducted? 
• What platforms are the chatbots hosted on? What are the privacy policies of these companies (e.g., Meta, WhatsApp, Telegram, Viber, etc)? 
• What is the metadata used for improving the function of the chatbot? How is this data minimised and deleted? 
• What safeguards are in place (e.g. trauma informed design)? 

5. Centring the needs and priorities of affected populations is essential

A recurring theme in our research was the question of what role automation plays in a humanitarian context, and the need to maintain human interaction even with the adoption of automated tools like chatbots.

Another issue that arose in the research was the lack of participatory, user-centred design practices that are inclusive of language and cultural contextualisation. Oftentimes, the people making tech like chatbots are not from the communities users are from, including language communities (if using machine learning). Further, chatbots can be deployed on populations before being adequately tested (or soliciting feedback) by the people who will need to rely on the tech to receive necessary services or information. 

Situations where chatbots are linguistically and culturally incompatible or make it difficult to access services (due to tech illiteracy, device access, error loops, etc) can potentially be avoided by considering if a chatbot is the right fit in the first place, given the specific needs and contexts of each situation. 

Our analysis surfaced a common tension—from our research, effective chatbots require time and resources to set up, but both of these tend to be in short supply when working in emergencies. This tension could benefit from additional research specifically focused on emergency situations. 

We’ll be publishing our full research report soon—follow us on Twitter or subscribe to our newsletter for updates! 

This is primarily because micro-enterprises are unable to access credit to grow their operations due to traditional loan processes, which rely on MSMEs demonstrating their creditworthiness through collaterals such as evidence of digital financial transactions and property. Their limited access to affordable formal credit results in poor working capital reserves, impeding their productivity and thwarting their expansion into mid-sized enterprises. This concerning phenomenon in turn hampers the overall economic growth of the country.

Cash flow–based lending could solve this critical credit gap

The cash flow–based lending approach could prove to be a viable alternative for MSMEs, especially micro-enterprises. Unlike traditional lending methods, this approach focuses on a borrower’s projected future and past cash flow data to determine their creditworthiness. This form of lending forms the core of India’s Open Credit Enablement Network (OCEN), an open protocol infrastructure that has the potential to democratise and transform India’s digital lending landscape and address the persistent challenges of the MSME credit gap, estimated to be at a staggering USD 800 billion.

An emerging digital public good being developed by Indian software industry think tank iSPIRT, OCEN streamlines the process of lending and borrowing by connecting a wide range of stakeholders in the lending ecosystem. Using the cash flow–based lending approach, it enables borrowers to share their data digitally, eliminating the need for traditional collateral requirements.

This makes it viable for previously unbanked and underbanked micro-enterprises to access credit through OCEN.

The implementation of OCEN depends on a diverse group of stakeholders, wherein each of them plays a unique role within the intricate lending infrastructure, bringing distinct expertise and performing specific functions. This includes:

1. Loan service providers (LSPs): Consumer-facing digital platforms—web- or application-based—that provide low-cost distribution, bringing knowledge of local and customer contexts to the network.
2. Technology service providers: FinTech companies that support onboarding to the OCEN protocol, roll out tailored credit programmes for MSMEs, and aid in technical implementation and adoption.
3. Account aggregators: Data intermediaries that act as consent brokers, allowing lenders quick access and plugging in of critical digital infrastructure for lending.
4. Underwriting modelers: Entities that assess vulnerability in terms of non-payment and late payment, supporting the decoding of raw data signals captured through digital trails.
5. Lenders: Banks, NBFCs, and small finance banks that provide capital and access to core banking networks, utilising the LSP infrastructure to increase last-mile connectivity and provide tailored credit solutions.
6. Borrowers: MSMEs or individual consumers who leverage credit options available within the LSPs’ platforms through secure digital processes, getting connected to multiple lenders through one platform.

Why should lenders opt for OCEN?

The adoption of OCEN could unlock substantial economic potential for the country. By 2023, MSME digital lending has the potential to increase by 10–15 times to reach INR 6–7 lakh crore (USD 80–100 billion) in annual disbursements. While OCEN makes a strong case for borrowers to be a part of the network, lenders too have a major incentive to join.

1. By capturing data digitally and doing away with the manual filling of forms, OCEN helps simplify the lending process. This enables lenders to offer customised loan products based on specific needs such as time, repayment schedule, and interest rates, benefitting both lenders and borrowers.
2. OCEN allows lenders to monitor the finances of the borrower in real time, helping them identify early warning signs for potential defaults. It automates key workflows such as loan disbursement and repayment tracking. Additionally, OCEN optimises the risk assessment process by enabling borrowers to share their data from various sources through consent-driven mechanisms without any in-person visits. This allows lenders to access and evaluate comprehensive, up-to-date credit histories, enhancing the efficiency of their evaluations. This makes it economically viable for formal financial institutions to underwrite low-cost, timely, and small-sized loans to the historically unbanked and underbanked, including micro-enterprises.
3. OCEN unlocks a large potential customer base of 190 million micro and small enterprise that lenders otherwise wouldn’t have access to. The operational efficiency of the process, when paired with the prospect of a diversified loan portfolio that OCEN will offer, makes small-ticket lending possible. This will enable lenders to expand their market reach and loan volumes.
4. By integrating with already-scaling account aggregators, OCEN can enable improved credit risk assessment, especially crucial for lending in the informal sector that constitutes approximately 40 percent of MSMEs.

However, while OCEN is a step in the right direction, it faces several challenges that can potentially restrict its popularity and viability.

1. Digital readiness and infrastructure

The effectiveness of OCEN depends on the degree of digital expertise possessed by micro-enterprises. For both lenders and borrowers to actively participate and avail the benefits of this credit system, it is imperative that MSMEs have digitally captured expenditures, income, and investments, also known as digital financial trails. These are imperative to determine a borrower’s eligibility for credit. An absence of these would render obsolete any incentive for lenders to participate in the model.

However, more than 90 percent of India’s population lacks basic digital literacy skills. In the MSME sector, the spectrum varies drastically, with some being completely unfamiliar with digital financial products while others use digital banking and transaction mediums such as UPI regularly. For instance, while approximately 33 percent of micro- and small enterprises are increasingly conducting their business on social media platforms, 60 percent of MSMEs are only beginning to move from cash to digital payments, indicating early stages of adoption.

In addition to this, even when digital literacy is present infrastructural shortcomings such as unstable electricity, sparse mobile tower coverage, and intermittent broadband connectivity limit the usability and reliability of digital tools, particularly in rural India.

An in-depth evaluation of the readiness of India’s micro-enterprises to adopt this new infrastructure is essential to ensure that the benefits of OCEN are within their grasp and align with the country’s financial inclusion agenda.

2. Cash-based economy

Even though OCEN makes it viable for financial institutions to underwrite low-cost loans for underbanked or unbanked MSMEs, the large number (an estimated 190 million people are unbanked or without formal bank accounts) is still an obstacle. In addition to this, approximately 72 percent of transactions in India are cash-based. India’s cash-based economy, along with a significant number of informal MSMEs, poses a considerable challenge to OCEN adoption. Without formal banking channels, MSMEs lack bank records and digital financial traits required by OCEN. The absence of formal business registration and records further limits accessible and reliable data for OCEN. This prevents MSMEs from establishing digital creditworthiness, which is crucial for OCEN adoption.

3. Data privacy

Even when digital readiness and formal banking channels are present, data privacy arises as an obstacle to the widespread application of OCEN. The OCEN framework relies on sharing sensitive financial information and personal data, leading to concerns about potential data breaches and misuses such as created and marketed tailored products based on financial information and size of the company by various agencies. As the volume of loan data increases, cybersecurity will become an increasingly big challenge, especially in the absence of any law on data protection in India. Including stringent measures such as encryption, secure storage, and strict adherence to privacy regulations can help combat this. Embedding privacy in design, and respecting user privacy, is equally essential. 

4. Apprehension among MSMEs

Transitioning to a digital platform is plagued with apprehensions and scepticism for the enterprises. Concerns around control, taxation, and transparency often prevent MSMEs from shifting to digital transactions. Since OCEN also calls for high transparency, there is concern among MSMEs about its potential consequence. Compilation of defaulter lists—those who are unable to pay back the loan in a timely manner—by OCEN also adds to this reluctance, and could lead to the exclusion of these individuals from the lending process. To address this, a large part of onboarding must focus on creating awareness about the network, and helping stakeholders understand its benefits. This can be done by leveraging networks of first movers and risk takers, and by incentivising MSMEs looking for lending through formal channels. Policy and regulations must also be relaxed to quell the anxieties of the stakeholders being onboarded.

Bridging the gap of the ‘missing middle’ in India’s MSME sector is both a significant challenge and an enormous opportunity that necessitates systemic transformation in lending approaches. OCEN emerges as a promising solution by leveraging digital data, reducing costs, and enabling better loan offerings. However, success hinges on MSMEs’ readiness for digital transformation and the cooperative and trusting relationship between the OCEN ecosystem and MSMEs. Achieving this collaboration can unlock a new era of financial inclusion, unleashing untapped economic potential and fuelling nationwide growth.

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Know more

• Learn more about how OCEN can enable small and marginal farmers.
• Read this article to learn more about how technology can drive financial inclusion.
• Learn more about digital solutions of India’s financial landscape.

However, tech companies are also some of the biggest carbon emitters. For example, producing semiconductors or silicone chips found in most gadgets and products today requires vast amounts of energy, which accounts for a majority of the carbon output from electrical devices. While many tech companies have announced net-zero policies, these are falling short by a long measure. In addition to this, advancements in AI and tech are further exacerbating global inequalities, as most of the social and economic benefits of AI are accruing to only a privileged few. 

On our podcast ‘On the Contrary by IDR’, we sat down with Jim Fruchterman of Tech Matters and Trisha Ray of Observer Research Foundation to discuss the role of technological developments in climate solutions, how tech giants influence this space, and what needs to change to enable countries to use tech more efficiently and for the benefit of all, especially those most vulnerable to the crisis. 

Below is an edited transcript that provides an overview of the guests’ perspectives on the show.    

AI can accelerate climate action

Jim: Currently, AI is mainly being used by the academic community to create climate models, to understand [the] climate crisis, to [analyse] how things are, [and] to [provide a] forecast into the future… [Technology provides a] lot of ways to deal with climate. I think people are using technology more broadly and [in] a lot of ways to deal with climate. The biggest area that you see [this happening in] is energy. People [are] trying to innovate around energy. Solar panels [too], which are not necessarily considered an advanced technology any more. [But it is] a big part of wind power.

Trisha: There is a lot of good that tech like AI can do. [It can] help us track, predict, and mitigate climate change. We could even use AI to invent new synthetic materials to replace the ones we currently use. [We can build] these new materials that are more resistant to heat or materials that can help us store solar energy better. There’s also a lot of interest in a concept called smart grid. And India has its own National Smart Grid Mission, where one could use AI to detect patterns in how people use electricity and how this may change over time, and then act accordingly.

However, unregulated use of AI can have serious consequences 

1. AI models have their own carbon footprint

Trisha: We talk about tech for climate in a way where we think it’s a clear-cut solution that will neatly fix all our problems. [But these] applications have a cost… One way to view this problem is to understand that it takes a lot of energy to train an AI model. There’s a study that attempts to quantify the carbon emissions [of AI]. And it says that training a single natural language processing model emits as much carbon dioxide as a car in its entire lifetime. That’s a lot.

2. The existing tech business model is leading to data colonialism

Trisha: The social and economic benefits of AI are accruing to a privileged few countries. A major concern of developing and underdeveloped countries is that [they] have data flowing out of their citizens, and they have services and products flowing in. So they’ve become data suppliers and product buyers. The name for this concept is data colonialism. Sub-Saharan Africa, Latin America, the Caribbean, South and Central Asia really falling behind in AI development and use in terms of start-ups, funding skills, and so on.

Jim: [Tech companies] farm you for your data and become billionaires. What we need to do is to see data and the AI that is built on top of that data [and] use [it] more in the interest of global society, local communities, patient states. Nithya Ramanathan and I have written several pieces on the need to decolonise data, aimed not at the companies because they’re doing total clinical data processing but at the government, at nonprofits, to understand that while they’re delivering community empowerment, this can’t be at the price of extracting the data from the community and then using it to punish that community.

3. Tech companies aren’t doing enough to offset their emissions

Jim: Tech companies are very powerful, and have this habit of killing legislation that works against their interests… We haven’t had any big privacy or data use laws passed in major countries, including India, in quite a while… And the tech companies, they have social good arms. But the idea of the social good arm is to say how do we spend, you know, 0.1 percent of our profits, or 1 percent of our profits. And then let’s not talk about what we do the other 95 percent of the time. And so when we’re doing social good, because we’re 10 or 15 years behind the times, we’re quite tiny in terms of our use of these models.

Trisha: [To combat emissions caused by tech] many major technology giants—Amazon, Microsoft, Alphabet, and Facebook—have all announced net-zero policies and initiatives. But there’s still a lot that needs to be done. And the first and most fundamental issue is that these net-zero initiatives often rely on something called the carbon offsets system. For example, if Amazon cuts 10,000 trees in a forest, it can plant a number of trees somewhere else in the world and declare that their net impact on the environment is zero. But that’s not how things work in the real world. There is no simple equivalency. And what Amazon should be doing is fundamentally changing how [it] operates, and being more transparent about the environmental impact of [its] operations.

What needs to change?

1. Using AI for communities

Jim: I want to see AI being used to help the local farmer, the local community leader, to better understand what’s going on, what might be going on, and help them make decisions within their own priorities and context. This is called a landscape focus—how do you actually make better things happen at the local level? And local leaders don’t think very much about climate. They’re thinking about how to increase farmers’ incomes when we have less water than we’ve ever had before. They’re kind of aware that things are changing, but they want to focus on local issues. And so a lot of this landscape approach is to try to make those local leaders, smallholder farmers, producers more powerful, and this is being led primarily by the traditional aid and biodiversity and conservation organisations, because they realise they’re never going to meet their goals if local communities are not making climate-smart decisions.

The best solutions come from the people who live with the problems.

Trisha: I think the best solutions come from the people who live with the problems. There’s an interesting example in relation to environmental crimes and the Amazon. The situation there is a little tricky, because you can’t always rely on the government or local authorities as they might often be the ones participating in displacing communities and encroaching on the forest. The Brazilian government also dismantled the organisation in charge of environmental monitoring and protection. So there’s not a lot by way of official resources, you can use. A nonprofit called Rainforest Connection has [installed] an AI in the Amazon to monitor ambient sound and alerts local team members if it detects logging or poaching activity [through] sounds [like] of chainsaws. So they’re partnering with the local indigenous tribes and their rangers to use that AI to monitor and then combat poaching and illegal logging.

2. Prioritising data sharing for social good

Jim: There is a need to shift the power from a unilateral extractive model to one where we understand that there’s such a thing as a rightful data owner, and that we’re going to use their data not, for instance, with checkbox consent, but with meaningful consent, so that meaningful benefits flow to that community. And I think one of the things that’s been identified as a big need is that we don’t have an easy way to share data right now for social benefit. So can there be an open-source licence or a Creative Commons licence for private data that actually gets used to benefit a farmer? So that they know that if their data is used, it’s actually going into a model that’s going to help their communities help them, more than it’s going to help the fertiliser maker, the maker of the agricultural equipment, the supply chain actor who’s going to try and by and large use data to get a lower price from them. How are we actually going to shift that power? And that’s a big theme for how I think the social sector should discard the for-profit business models and actually engage in empowering the communities that they serve.

3. Building tech fluency

Trisha: A big problem with policymakers is that there’s a lack of tech fluency. It is the duty of industry, of civil society, of media, to highlight these issues. One example is the energy impact of bitcoin mining. It’s everywhere. We’re all now aware of it and that’s because it was [covered vastly] in the media. There is a role that media can play in bringing some of these more niche issues on to our radar. We may also want to think about whether we should even be using AI in some situations. There are two fundamental principles of international humanitarian law. These are necessity and proportionality. So is AI necessary in a given context? Are there alternate solutions that are low-tech, but perhaps less intrusive? Is the sheen of the neutrality of AI solutions distracting from some harmful consequences that they have? We should be delegating these solutions rather than implementing them blindly.

4. Using popular media to create public awareness on tech and AI

Jim: I think the role of Hollywood and Bollywood in changing consumer opinions should not be discounted. And one of my early backers is a guy named Jeff Skoll, who was one of the first two people at eBay. He created a movie company in Hollywood that was going to highlight social issues, and we all joked about how much money he was going to lose trying to do this. And Participant Media has actually really worked. And, you know, he backed An Inconvenient Truth, which was one of the more influential films, and certainly in the United States, about awareness of climate change. I do think that it’s not just traditional news media, but it’s also our film industries that are often sort of opinion and sentiment leaders if they can turn something into a story; it’s not easy to turn AI into a story unless it’s, you know, the Terminator movies or something like that. So we have to kind of think about this, but I believe it does move a society.

You can listen to the full episode here.

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Know more

• Read this article to learn more about how AI is assisting reforestation effort.
• Read this to learn more about the relationship between ICTs and climate change.
• Read this article to learn more about climate tech start ups in India.

Over the last seven years, we at Koita Foundation have worked closely with more than 15 nonprofit organisations on a vast range of tech-enabled solutions including building applications for on-the-ground workers to manage their operations, developing business intelligence (BI) analytics platforms, and deploying project management tools.

Here are some steps that can serve as a guide for building what we call tech capacity in an organisation.

1. Talk to peers who have done this

Reach out to other organisations that are doing similar work and have successfully implemented technology. Talking to other leaders helps one understand the complexities and advantages of adopting technology and allows leadership to get comfortable with the idea of tech within one’s organisation. 

2. Scope the right focus areas to use technology

Most organisations have several pain points that can be addressed with the use of technology. It is very important for the leadership to identify areas where tech implementation is likely to be most beneficial. Over the years, we have found that the highest return on effort and investment is in helping organisations use technology for their ‘front-end’ processes (for instance, how the organisation engages with the communities that it works with) rather than ‘back-end’ processes (for instance, finance and HR). The optimisation of front-end processes drives overall operational efficiencies, quality, and scalability. This in turn creates a virtuous cycle with communities, donors, and other stakeholders.

3. Ensure that this is a high priority for the CEO and senior leadership

This is critical. For an organisation looking to go digital, the single most important prerequisite is the commitment of the CEO and the leadership to the idea. The second most important criterion is buy-in from the team.

Digital transformation demands a great deal of change management. It requires perseverance, and the process can be highly uneven and full of ups and downs. Without wholehearted support from and navigation by the CEO and senior leadership, such a transformation is unlikely to be successful.

While it might be easier if the leadership has some form of tech background or experience, it is more important for them to commit some time to engage with the technology team or external vendors and get involved in the process.

4. Bring on a tech adviser

Most organisations would benefit from having a tech adviser who can help them navigate this journey. Board members are usually a good source to tap into the technology network when looking to hire tech consultants.

The adviser can help with key aspects such as identifying the right focus area for using technology, streamlining business requirements, building a road map to completion, thinking of build vs buy options, and identifying suitable tech vendors.

If one just approaches a vendor, they don’t have the ability to identify the right focus area for the organisation or rationalise requirements, and will deliver whatever you ask them to. This is similar to bringing a building contractor to start work without having a clear design from an architect. This can create a lot of rework later, which is both costly and time-consuming.

5. Form a core team

In addition to leadership buy-in, organisations need to put together a small core team that will take ownership of this process of business transformation. The team can comprise two to three people, including the head of the particular programme for which tech is to be implemented.

This core team does not need to have tech experience, but senior leadership should look for people who have the aptitude and inclination required for problem-solving and quality/process improvement. Being able to think from the users’ perspective and knowing how to persuade field teams to work with technology are also useful skills to have.

6. Engage with the end users of the technology

Part of building tech capacity is engaging and training users (such as the field teams) who need to use the technology tools correctly and consistently. In addition, the organisation should provide relevant tech support during the roll-out of the tools. Vipla Foundation, a nonprofit we worked closely with, ran donor and volunteer campaigns to source smartphones for their balwadi teachers. They made sure that the teachers had working mobile phones and then created a structured training plan so that all the balwadi teachers understood the value of the overall programme, why it was important for students and teachers, and how digitisation would benefit them, in addition to taking them through all the features of the app including offline functionality. This approach helped the balwadi teachers see the big picture and subsequently support the initiative, although it did take some additional effort from them to learn and use a new technology tool.

This sustained support is important to make sure that all your teams—from the head office all the way to the field—feel comfortable around the technology, and to remove any misconceptions they may have about why the organisation is moving towards digitisation.

7. Find the people to do this

There are a couple of ways to do this. One is to look for people internally. While a person with a programme or product management background is a suitable choice for leading this digitisation, many nonprofits might not have these people in-house. This has happened at Antarang Foundation as well as at Vipla Foundation where operations managers with a tech bent of mind have acted as the point of contact and translated the nonprofit’s requirements to the software developer.

Another approach is to look for this talent externally. It can be hard to recruit a CTO for a nonprofit set-up unless the leadership and/or board members know of people who would like to cross over into the development sector. Location plays a role too—in large cities such as Mumbai and Bangalore, finding a tech adviser may be easier than in Tier-II or Tier-III cities.  

While it is hard to get a CTO on board, nonprofits can hire for junior positions to handle the technical aspects of the programme, such as testing and maintenance. These junior employees can then report to the tech-literate operations or programme manager.

8. Finally, find money to fund this

Raising funds for tech is hard especially when your donors are more interested in funding programmes. This is especially true for nonprofits that struggle with reporting impact to donors—how do you ask for funds for tech when you do not have the very data (that tech can help you acquire) to prove that you need digitisation to run your programmes better?

What we have seen often is that once nonprofits have some tech tools in place, the data from those tools can be used to build the case and convince donors for additional funding. Hence, it is important to start technology initiatives conservatively with very clear objectives and ensure high focus on their success.

We need to start looking at technology as a strategic tool that can help the organisation evolve from manual, non-standard operations to a data-driven culture based on standardised processes and reliable data collection through appropriate tools. As leaders build strategy, technology should be an integral part of it, and not an afterthought.

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Know more

• Read this article to learn more about the challenges of building technology teams in nonprofits.
• Read this article to learn whether your nonprofit should invest in custom-built or pre-existing tech solutions.
• Read this article to learn about a nonprofit that is using tech to bring education to marginalised girls.

Weak internet connectivity, interrupted electricity, outdated devices, and a lack of tech literacy were among the various issues that teachers experienced owing to their financial resources and geographical location. These situations were largely identified in schools located in rural and remote areas or government schools in cities. A survey conducted in 2020 across six Indian states found that 36 percent of teachers listed access to tech infrastructure in schools as a barrier to remote education.

The discourse around strengthening remote learning has largely been focused on students’ experiences, but teachers must also be integrated into the process. EdTech solutions have enormous potential to deliver quality at scale and contribute to a resilient education system. However, this potential can only be fully realised through the proactive participation of teachers. Bringing teachers on board requires investments in making EdTech products easy to use, affordable, and engaging.

We have solved for access. What next?

In the initial months of the pandemic, the main challenge that teachers and schools faced was around training and resources. Government schools in rural and urban India were underfunded, underequipped, and underprepared for the abrupt shift to remote learning. During the pandemic, as many as 50 percent of teachers were spending more money on teaching materials than before the schools closed. In cases where devices existed, they were not optimised for smooth connectivity.

The government and private sector were quick to act. From 2020 to 2022, the market size of EdTech products increased from USD 0.75 billion to USD 2.8 billion. This figure is estimated to grow to USD 4 billion by 2025. Public initiatives such as DIKSHA and free access to the National Digital Library, among others, provided a much-needed push. There was a 30 percent increase in screen time spent on education-related phone apps during the lockdown. Moreover, the user base for EdTech increased, particularly for the K-12 segment (primary and secondary education), which grew from 45 million to 90 million during this time.

With EdTech providers experiencing more reach, they must now focus on improving the quality of their solutions. This includes understanding local contexts, aligning with the state curriculum, and creating relevant lesson plans. The products must take a user-centric lens to increase chances of uptake. In our experience, maximising the reach of EdTech solutions among teachers hinges on getting four processes right.

1. Identifying the change agent

Community-based stakeholders such as self-help groups or anganwadi workers (AWWs) can deliver maximum impact as they have a pre-existing relationship with the local community. This is especially true in rural or low-literacy environments, where external organisations or nonprofits may be met with suspicion. India has 13.9 lakh anganwadi centres, which cater to a massive slice of the population and are crucial in driving early childhood care and education. In our experience, AWWs are curious and resourceful and enjoy close ties within the community, making them the ideal change agents.

However, including them in intervention design and implementation is not enough; organisations must earn their trust to win the vote of the larger community. For instance, Rocket Learning facilitators, who run several teacher peer groups, share daily learning activities and AWWs respond with videos of students carrying out those tasks. Play-based learning encourages proactivity and engagement from the AWWs. Tapping into the aspirations and motivations of AWWs is also key to their full involvement. Many of them want to be empowered with the resources that allow them to impart foundational learning and be taken seriously as educators. It is necessary to account for this in the programme design by including upskilling opportunities for field workers and on-ground implementers and encouraging the workers’ personal development in the process.

2. Nudging teachers towards the right behaviours

Encouraging teachers based on behavioural insights can play a key role in influencing what is happening inside classrooms. This can range from personalised assessment reports that track performance to digital badges or certifications that reward engagement. These mechanisms add a ‘human’ element to EdTech products and reinforce desirable behaviour.

Quantitative data can help draw out patterns not just to design nudges but also to optimise them.

Designing effective nudges requires substantive data, both qualitative and quantitative. Human-centred surveys and qualitative research helps root interventions in local contexts. For instance, tracking teachers’ daily, weekly, and monthly activities, priorities, and needs can help organisations identify the touchpoints to double down on.

Meanwhile, quantitative data can help draw out patterns not just to design nudges but also to optimise them. Data can allow EdTech providers to zero in on the most favoured communication channels (YouTube, WhatsApp, etc.), format (voice, text, or multimedia), or time of day, and leverage these preferences for maximum impact. Monitoring and evaluation help factor in changing patterns and update and fine-tune these over time. This encourages teachers and parents to conduct different activities with children, share proof of completion, and receive ‘rewards’ or ‘certificates’ upon achieving targets.

3. Creating holistic digital communities

Learning cannot take place in siloed teacher–student and parent–student interactions. It is necessary for teachers and parents to be in sync with the students’ learning progress and achievements, especially in a remote learning scenario where the boundaries between the classroom and home are blurred.

While setting up digital communities, low-capacity settings are ideal to utilise existing structures. Used by more than half of the students and 89 percent of teachers, WhatsApp, for instance, emerged as the most popular channel of communication. Designing simple solutions that build on top of these patterns make them cost-effective and increase the likelihood of participation. It is also especially beneficial for parents who depend on daily-wage labour and cannot skip work to attend physical meetings.

Creating a constant feedback loop between teachers and parents is effective in helping establish positive parent–teacher, teacher­–supervisor, and teacher–child relations. This also creates a supportive community that parents and teachers can lean into to share their concerns and vision for the child’s learning journey.

4. Working effectively with governments

Working with governments is the best way to deliver impact at scale. Market-driven EdTech solutions tend to prioritise users who can afford to pay for their products. Meanwhile, organisations looking to deliver services to marginalised communities often face resource limitations. Government support can bridge this gap between intention and impact, ensuring fairness in terms of student usage, learning activities, and teacher training. Having said that, it can be time-consuming to ensure state willingness and capacity to roll out such programmes at scale. Moreover, EdTech organisations seeking to implement solutions in government schools have to contend with formal rules and informal practices that vary from state to state. Integrating the hardware, product, and lesson plans into the existing public school ecosystem often requires approvals and negotiations at multiple levels.

Organisations and governments working in these spaces must project teachers as the face of the intervention.

Aligning with state priorities and designing focused interventions across the governance hierarchy—from the anganwadi teacher and their supervisor to the project-, district-, and state-level officials—is critical to keeping the system engaged. Additionally, organisations cannot copy-paste intervention templates that worked in one state or district on to another. It is necessary to gauge the motivations and priorities of each administration before formulating an approach. Recognising the unique needs and operating environment of the state to tailor the plan is a vital step in ensuring meaningful engagement. 

Ensuring EdTech uptake among teachers in rural areas with no experience or tech receptiveness is challenging but necessary. The World Bank’s EdTech Readiness Index recognises this, listing teachers as one of the six pillars to be strengthened for best practices in policy and application. Prioritising teachers’ motivations and attitudes, and pushing for a broader mindset shift, will play a major role here.

Most importantly, it is necessary to dispel fear and suspicion around tech. EdTech services should not be presented as a replacement for teachers, but as a resource that complements their work and helps ease their burden. Organisations and governments working in these spaces must project teachers as the face of the intervention as much as possible. Only through educator buy-in and engagement can we tap into the full power of EdTech to transform students’ lives and work towards building a resilient education ecosystem.

In order to build and sustain teachers’ engagement, it is imperative to position technology and EdTech as tools that can build and further augment teacher capacity. We need to leverage data from such solutions to build positive loops in terms of teacher behaviour and be cognisant of region-specific nuances for the design-to-implementation journey of EdTech solutions.

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Know more

• Learn how teachers’ well-being can be promoted in India.
• Read this report to read about the impact of EdTech on the country’s education landscape.
• Learn how upskilling trends get influenced by EdTech solutions.