Development and Validation of AI instructional Platform Using ADDIE Model for Enhancing Pre-service Biology Teachers Learning Outcomes in Collaborative Learning Settings in North-East, Nigeria
This study developed and validated an AI instructional platform using the ADDIE model, demonstrating that its integration with collaborative learning strategies—particularly peer tutoring—significantly improved academic achievement in Genetics among pre-service Biology teachers in North-East Nigeria compared to conventional teaching methods.
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In the world of education, a persistent challenge has long been how to teach complex, abstract ideas effectively. Biology, the study of life, is filled with such concepts. Topics like genetics, which explain how traits are passed from parents to offspring, often feel invisible and difficult to grasp because they cannot be seen with the naked eye. For decades, teachers have relied on standard lectures and textbooks, but many students still struggle to visualize these processes or connect them to the real world. At the same time, the rise of artificial intelligence has promised to revolutionize learning by offering personalized help and instant feedback. However, simply handing a student a smart device does not guarantee they will learn better. The real question is how to weave this powerful technology into the social fabric of the classroom, where students learn best by talking, arguing, and solving problems together.
A team of researchers in North-East Nigeria set out to answer this question by building a custom digital tool designed specifically for future biology teachers. They focused on the difficult topic of genetics and asked whether a computer program could help students learn better when combined with specific ways of working together. The team did not just test an off-the-shelf app; they built their own platform from the ground up using a systematic five-step planning process known as the ADDIE model. This approach ensured that every feature of the software was chosen to match the needs of the students and the goals of the biology curriculum. The researchers then tested this new system against traditional teaching methods in four different colleges, involving hundreds of student teachers to see if the digital approach truly made a difference.
The development of this platform was a careful, step-by-step journey. First, the team analyzed what the students needed and what the biology curriculum required, identifying that genetics was a major stumbling block. They then designed a system where students could work in groups, share notes, and get immediate help from an intelligent assistant built into the software. During the building phase, the researchers created a digital environment where students could join video calls, collaborate on documents in real time, and receive personalized hints when they got stuck. Before the tool was ever used in a classroom, it was rigorously checked by experts in education and computer science. These experts confirmed that the content was accurate, the software was easy to use, and the design was suitable for teaching. The platform received a near-perfect score for its quality, proving it was ready for real-world use.
Once the tool was validated, the researchers put it to the test with 361 student teachers. They divided the students into different groups to see which learning method worked best. One group used the new AI platform while working in pairs to discuss ideas. Another group used the same platform but followed a "jigsaw" method, where each student became an expert on one part of a topic and then taught it to their teammates. A third group used the platform for peer tutoring, where more knowledgeable students helped those who were struggling. A fourth group, serving as the control, learned genetics using the standard, conventional method of lectures and textbooks without any special digital tools. All groups took a test before the instruction began and again after twelve weeks to measure what they had learned.
The results showed a clear and significant advantage for the students who used the AI-supported platform. Those who learned with the help of the digital tool and collaborative strategies scored much higher on the final test than the students taught with traditional methods. Among the different strategies, the peer tutoring approach produced the highest scores, with students in that group outperforming the traditional group by a wide margin. The other collaborative methods also showed strong improvement, with the pair-work and jigsaw groups scoring significantly better than the control group. The data indicated that the combination of smart technology and human cooperation created a learning environment where students understood the difficult concepts of genetics far better than they did with standard teaching alone.
This study demonstrates that artificial intelligence does not need to replace the teacher or isolate the student to be effective. Instead, when the technology is carefully designed to support students working together, it can amplify the benefits of collaboration. The researchers found that the key was not just having the software, but how it was used. By integrating the AI tool with proven methods like peer tutoring, the platform helped students explain concepts to one another, receive instant feedback, and build a deeper understanding of complex biological systems. The success of this project suggests that for difficult subjects like genetics, the future of learning lies in a balanced approach where smart tools and human interaction work side by side to make abstract ideas clear and accessible.
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