New Perspectives in Science Education

Abstract

The rapid integration of generative artificial intelligence (GenAI) into higher education presents opportunities to rethink established approaches to teaching, learning and assessment. As AI becomes increasingly capable of generating scientific explanations, analysing data and producing sophisticated academic outputs, educators face a fundamental challenge: how can learning experiences be designed to preserve intellectual engagement, develop critical thinking and ensure meaningful assessment? Addressing this challenge requires a shift in emphasis from technological innovation towards purposeful instructional design and constructive alignment.

Drawing on practical experience implementing two AI-enhanced experiential learning approaches in undergraduate and postgraduate bioscience education at Khalifa University, this reflective analysis examines the pedagogical insights emerging from their design, implementation and evaluation. Informed by Kolb’s experiential learning theory [1], the approaches were developed to encourage active inquiry, critical evaluation and independent scientific reasoning.

In postgraduate Research Methods, AI-generated simulated datasets tailored to individual research projects enabled students to formulate hypotheses, justify statistical approaches, interpret unexpected findings and reconsider experimental designs. In undergraduate Metabolic Biochemistry, collaborative AI-assisted infographics exploring unfamiliar metabolic pathways incorporated structured verification, peer discussion and reflection to promote critical engagement with scientific information.

Several important pedagogical insights emerged. First, effective AI integration required greater attention to constructive alignment [2] between intended learning outcomes, instructional activities and assessment criteria. Second, the sophistication of AI-generated outputs highlighted the limitations of product-focused assessment and the importance of evaluating students’ reasoning, verification and decision-making processes. Third, unexpected findings and AI-generated inaccuracies, initially viewed as technological limitations, offered valuable opportunities to design activities centred on questioning, investigation and critical reflection. Finally, experience across both educational contexts reinforced the importance of instructional scaffolding, feedback and clearly defined expectations in maintaining students’ intellectual ownership of their work.

These experiences highlight a fundamental shift in the role of educators in an AI-enabled learning environment. Effective integration of GenAI requires more than incorporating emerging technologies into existing practices; it demands critical examination of how learning is designed, supported and assessed. The lessons learned offer practical insights into the importance of constructive alignment and intentional pedagogical design in developing meaningful, intellectually challenging learning experiences in higher education.

Keywords: Generative Artificial Intelligence; Constructive Alignment; Instructional Design; Assessment; Experiential Learning

References

[1] Kolb, D. A. Experiential Learning: Experience as the Source of Learning and Development. Prentice Hall, 1984.

[2] Biggs, J. Enhancing teaching through constructive alignment. Higher Education 32, 347–364, 1996.