From chalkboards to chatbots in Nigeria: 7 lessons to pioneer generative AI for education
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By Adesina Wahab
In 1984, educational psychologist Benjamin Bloom demonstrated that students receiving one-on-one tutoring vastly outperformed their peers confined to the traditional classroom setting. Despite these proven benefits, the prohibitive cost of providing individualized attention to every student has remained a significant barrier for most families.
Forty years on, with the rapid advent of generative artificial intelligence, many countries have an opportunity to reach the ideal of one-to-one tutoring. Today, large language models (LLMs) can interact with students personably and adapt to their learning needs.
While recent studies highlight AI’s potential to enhance learning outcomes, they often fall short in two key areas. First, they focus predominantly on developed countries. Second, they rely on specialized but unaffordable software. However, unlike traditional AI, which excels at pattern recognition and prediction, generative AI can create new human-like content, opening up broader possibilities for application in education.
Leveraging generative AI in Nigeria
Building on this potential, a recent experiment in Edo State, Nigeria, represents one of the pioneering efforts to leverage free generative AI tools to enhance educational outcomes in a context where it’s most needed.
Over June and July 2024, 800 first-year senior secondary students attended after-school English classes in computer labs twice a week. Each session began with the teacher introducing the week’s topic, followed by students interacting with Microsoft Copilot, a generative AI tool powered by ChatGPT, to master the selected topics comprising both grammar and writing tasks. Acting as “orchestra conductors” of the pilot, the teachers guided students in using the LLM, starting each session with a suggested prompt. As the students interacted with the AI, the teachers mentored them, offering guidance and additional prompts. They also led brief reflection exercises at the end of each session.
The PIONEER acronym summarizes the initial lessons learned during the implementation:
1. Prioritizing students. The students in the pilot were highly engaged, with many expressing a strong desire to spend more time in the computer lab using the AI tool. Teachers noted that students quickly found unique and productive ways to interact with the LLMs. Even students in the control group were eager to participate, highlighting the program’s potential to boost academic engagement and prioritize students’ development of essential skills like digital literacy and critical thinking for their future. Imagboghowan Anointed, a student at Imaguero College in Benin city told us: “The program has helped me with my communication. My interaction with teachers, friends, and families. It has helped me with my pronunciation, writing, spelling, and vocabulary.”
2. Inspiring teachers. Following the pilot, teachers’ initial apprehension about using AI shifted to recognizing its potential and being aware of its guiding role in boosting learning amongst their students. Encouragingly, teachers formed informal groups to share best practices and improve along the way. They are beginning to see AI as a technology that can add value to their roles. Okhide Eugene, a teacher at Imaguero College in Benin City, told us that AI is “like an assistant teacher. In our current program, we supervise what the students are doing.”
3. Optimizing immersion. The program ran for six weeks, but a longer duration could have been more effective. In the first weeks, students focused on setting up emails, creating Microsoft Copilot accounts, and learning to use the computers, as many had never used one before. By prolonging the program, more time can be allocated to focus solely on the students’ actual learning needs.
4. Nurturing necessary infrastructure. As far as technology can go, schools need essential electrification and connectivity. Frequent power and internet outages, often exacerbated by the rainy season, disrupted student interactions with the LLMs. Backup power and connectivity for classrooms were crucial to maintaining smooth, uninterrupted sessions.
5. Empowering participants with relevant materials. In innovative programs, teachers and students need support to succeed. As part of the program, toolkits for students and teachers were developed to guide the sessions. The guidance suggested prompts to encourage productive engagement, allowing the LLM to act as a tutor and adapt to the students’ levels. The prompt engineering made the LLM’s responses much more useful to the students, with relevant examples to the local context and environment, and made the sessions more structured and effective.
6. Enhancing implementation. As with any program, the gap between design and implementation can be significant. To address this, a small team of monitors closely supervised each session, gathering key insights and providing feedback to ensure the program stayed on track.
7. Reducing AI risks. While enjoying AI’s effectiveness as classroom innovation, teachers highlighted key dangers of AI, like overreliance, hallucination (generating false responses and presenting them as facts), and misuse, with careful attention to mitigation strategies helpful to students as they navigate this new way of learning.
These seven PIONEER lessons learned from the Edo State pilot offer valuable insights into the future of AI in education, particularly in resource-constrained environments.
Culled from World Bank blog
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