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

Authors

  • Doreen Khrystel P. Gonzales Graduate Student, Department of Science and Mathematics Education, College of Education, Mindanao State University-Iligan Institute of Technology, Iligan City, Philippines Author https://orcid.org/0009-0008-6453-9263
  • Douglas A. Salazar Department of Science and Mathematics Education, College of Education, Mindanao State University-Iligan Institute of Technology, Iligan City, Philippines Author https://orcid.org/0009-0005-3430-5616
  • Manuel B. Barquilla Department of Science and Mathematics Education, College of Education, Mindanao State University-Iligan Institute of Technology, Iligan City, Philippines Author https://orcid.org/0009-0009-8772-0222
  • Edna B. Nabua Department of Science and Mathematics Education, College of Education, Mindanao State University-Iligan Institute of Technology, Iligan City, Philippines Author https://orcid.org/0009-0002-0069-210X
  • Monera A. Salic-Hairulla Department of Science and Mathematics Education, College of Education, Mindanao State University-Iligan Institute of Technology, Iligan City, Philippines Author https://orcid.org/0009-0002-9535-2465
  • Vanjoreeh A. Madale Department of Science and Mathematics Education, College of Education, Mindanao State University-Iligan Institute of Technology, Iligan City, Philippines Author https://orcid.org/0009-0002-0229-755X

DOI:

https://doi.org/10.56294/mw2025815

Keywords:

AI Integration in Education, AI Feedback, Bioenergetics, Science Education, Module learning

Abstract

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.

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Published

2025-10-10

How to Cite

1.
P. Gonzales DK, Salazar DA, Barquilla MB, Nabua EB, Salic-Hairulla MA, Madale VA. Integrating Generative AI into Bioenergetics Modules: A PAIRR-Based Approach for Senior High School Biology. Seminars in Medical Writing and Education [Internet]. 2025 Oct. 10 [cited 2025 Oct. 20];4:815. Available from: https://mw.ageditor.ar/index.php/mw/article/view/815