AI Entered the Chat: Is It Too Much Too Soon for East Africa’s Classrooms?

As artificial intelligence (AI) is rapidly entering East African classrooms, students and teachers face both unprecedented opportunities and new inequalities in digital learning (Ahmed et al., 2025). Across Tanzania and East Africa, digital transformation in education remains incomplete. Over the past decade, and especially since the COVID-19 pandemic, institutions have invested in connectivity, platforms, and devices. However, access alone does not guarantee meaningful participation, and research shows that foundational digital literacy among teachers and students is uneven, shaped by infrastructure gaps, challenges of having to switch between several languages in teaching situations, and limited pedagogical support (Mutebi et al, 2023; Nkonoki, 2026; Nkonoki & Meena, 2026). At the same time, the use of generative AI, automated feedback, and adaptive platforms are becoming widespread. For education systems in the Global South, this raises urgent questions: Is driving for AI literacy premature in contexts where digital literacy itself is incomplete? Does early AI adoption risk deepening inequality, or does postponing it reinforce global disparities? Having this discussion is important because without deliberate integration there is a risk of exacerbating the existing digital and linguistic inequalities.

In my discussion here, I introduce the idea of AI as a potential “fourth-level digital divide” hindering digital inclusion, and I argue that the challenge lies not so much in the rapid pace of AI adoption, but in finding ways to deliberately integrate AI into the students’ digital literacy journey so that it strengthens local capacity, ensures equity, and avoids deepening structural divides. In my view, achieving this requires that AI literacy should evolve in parallel with digital and data literacy. My argument is grounded in recent regional research and digital divide theory, and it reflects lived educational realities in the Tanzanian and the broader East African contexts. I hope this perspective encourages policymakers and educators to prioritize equitable, context-sensitive AI integration that strengthens teacher capacity, infrastructure, and critical digital skills instead of rushing adoption without considering its implications.

The unfinished digital literacy agenda and AI as a potential factor of further division

Evidence from Tanzanian and East African TVET contexts highlights the layered nature of digital transformation (Nkonoki, 2026). As I see it, there are five challenges related to this. Firstly, there are gaps in access as internet connectivity remains unreliable in rural areas and devices and technical support are limited, causing a situation that digital divide theory calls the first-level divide (van Dijk, 2005). Secondly, there are disparities in the students’ skill levels as their competencies, for example, in navigating learning platforms, evaluating information, managing communication, and practicing data safety develop unevenly, reflecting the second-level divide in digital skills and usage (van Dijk, 2005). Thirdly, beyond access and skills lies the third-level divide of inequalities in outcomes from digital engagement (Van Deursen & Helsper, 2015). A fourth restriction developing digital skills is the language barrier, visible in how most educational technology operates in English but many learners are more comfortable using Swahili. Because of this, without localization there is a risk that the used digital tools reproduce exclusion (Nkonoki & Meena, 2026). Finally, the fifth challenge is that despite recognizing the potential of digital learning many teachers report limited professional development and uncertainty about pedagogical integration (Nkonoki, 2026). Digital literacy is therefore a relational, pedagogical, and contextual challenge, not merely a technical challenge.

Even though AI promises personalization, content translation, and automated feedback, research on the topic cautions that instead of eliminating inequalities, new technologies often reorganize them (van Dijk, 2005). As a result, I suggest that AI may introduce a fourth-level of digital division by causing disparities in understanding and in skills of critical evaluation, and by shaping algorithmic systems in a new way. Educators and learners with better connectivity to devices, stronger digital confidence, and higher English proficiency will benefit the most, while others may end up using AI in a more uncritical way, and as a result they may be more susceptible to its potential dangers.

Did AI enter the “Global South” chat too soon?

In contexts of the Global North, AI builds on established digital infrastructures. In much of the Global South, however, foundational digital infrastructures and skills are still being developed. At the same time, AI has already arrived in East African classrooms and on the online learning platforms they use, and exposure is happening whether schools officially integrate it or not. Educators and learners are encountering it formally in classrooms and informally online. In my view, adopting AI happens “too soon” only when introduced without the digital skills needed to navigate, evaluate, and critically engage with its outputs. Unfortunately, this is the situation now. Waiting for perfect digital literacy risks leaving students to interact with AI in a way that is unmediated and happens outside educational guidance. The solution is to avoid taking shortcuts and to refuse the urgency for early adoption to drive our actions. Instead, we should aim for deliberate integration by building foundational digital literacy and critical data skills in parallel with AI awareness, so that learners can gain both competence and agency, instead of being left behind or exposed to inequality.

Overall, I think the most important things to do in the process of integrating AI into low resource, unequal digital literacy contexts are the following:

• Investing in infrastructure and teacher capacity.
• Embedding AI and data awareness within existing digital literacy curricula.
• Developing policies on data protection and academic integrity.
• Ensuring linguistic and cultural contextualization.
• Framing AI as a mediated pedagogical tool, not a substitute for human agency.

Transformation should be measured not by speed of adoption, but by the depth of inclusion and critical capacity it cultivates. AI literacy is therefore an extension of digital literacy, evolving hand in hand with data literacy acting as a necessary foundation. Data is the backbone of AI, and because of this it must be included in the equation as early as possible.

Integrating foundational digital literacy and AI literacy is key

East Africa’s digital literacy journey remains unfinished. Infrastructure gaps, language barriers, and uneven pedagogical preparedness continue to shape participation. Instead of eliminating these challenges, AI intensifies them. Reaching AI literacy becomes problematic only when detached from the practical contexts where the applications are used, and when it is not taught together with foundational digital competences. It also becomes delayed when learners encounter AI without guidance. Hence, the pedagogical task is integrative: strengthen digital literacy while cultivating data and AI awareness in parallel. This approach resists technological marginalization, and it can foster both adaptation and epistemic agency. In low-resource settings, progress is measured by equitable, contextually grounded, and critically informed engagement with emerging technologies.

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