Longitudinal AI Insights

Today, I would like to share a recent AI SoTL article entitled, “Longitudinal insights into AI in education: Usage, ethics, and policy development in higher education” by Parker et al. (2026) (https://doi.org/10.1016/j.caeo.2025.100329 ).

The authors track how higher ed students’ use of AI evolves over time, with implications for learning design and instructional practice. Over multiple semesters of data from a teacher ed program, researchers documented significant increases in the use of AI tools for studying and assessment preparation, indicating a shift in how learners incorporate AI into self-regulated learning processes and academic workflows. Usage rates for AI in assessment preparation grew substantially, suggesting students increasingly treat these tools as part of their distributed cognitive resources.

Findings

Across multiple semesters, the study found a substantial and sustained increase in students’ use of generative AI tools for academic tasks, particularly for assessment preparation and content clarification. Students increasingly integrated AI into drafting, summarizing, and studying activities, effectively incorporating these tools into their self regulated learning workflows. Quantitative trends indicated that AI use shifted from exploratory engagement to routine academic support over time. At the same time, the data showed variability in how strategically students used AI, with stronger outcomes associated with task specific, iterative prompting rather than passive content generation. The findings suggest that AI is becoming embedded within students’ distributed cognitive systems, reshaping study behaviors and academic performance patterns while reinforcing the need for instructional scaffolds that guide productive, metacognitive engagement.

From a learning sciences perspective, this trend highlights the importance of scaffolding AI use within instructional design so that these tools support metacognitive monitoring and strategic engagement with content rather than functioning as undirected black boxes. The study also finds that students’ ethical perceptions of AI use remain unsettled, which suggests educators must create opportunities for reflective dialogue about AI as part of developing robust learner agency and digital literacy practices. In designing AI-infused learning environments, aligning tools with clear learning objectives and assessment criteria remains essential to promote deeper engagement and robust skill development.

Reference

Parker, L., Loper, A. J., Carter, C. W., Hayes, J., & Karakas, A. (2026). Longitudinal insights into AI in education: Usage, ethics, and policy development in higher education.Computers and Education Open, 10, 100329. https://doi.org/10.1016/j.caeo.2025.100329