Integrating Generative AI into Bioenergetics Modules: A PAIRR-Based Approach for Senior High School Biology

Introduction: artificial intelligence (AI) offered transformative potential for enhancing learning and academic support, but its systematic integration into science-specific modules aligned with DepEd guidelines had not been fully explored. Bioenergetics, a complex topic in senior high school biology, remained a persistent challenge for learners.Methods: the study developed a Generative AI–integrated bioenergetics module using the Peer and AI Review + Reflection (PAIRR) approach. A mixed-method design combined quantitative analysis with qualitative support. Module validation assessed Student Involvement Index, feedback-based readability, and Communication Index. A limited trial was conducted with 64 11th grade learners. Achievement scores before and after module use were analyzed using the Wilcoxon Signed Rank test, and post-implementation interviews captured learner perceptions.Results: the module achieved acceptable validation scores for Student Involvement Index (1,08), feedback-based readability, and Communication Index (0,003). Learners’ achievement scores improved significantly after using the module (p < 0,05). Students reported that AI provided personalized support, clarified complex bioenergetics concepts, and enhanced engagement. Challenges included intermittent internet connectivity, uneven group participation, time constraints, and initial difficulty using ChatGPT.Conclusions: the Generative AI–integrated module effectively enhanced learners’ understanding of bioenergetics, demonstrating AI’s potential as a collaborative peer in science education. Further research should evaluate scalability, long-term impact, and adaptability across other science topics.