This commentary proposes a four-phase framework (Establish Relevance, Technical Details, Intuition, and Practice) for aligning the instructional structure of learning evaluation experiments, and examines how the probabilistic nature of large language models complicates, without invalidating, structural control in GenAI research.
This paper argues that the core problem extends beyond academic dishonesty to a deeper misalignment between assessment practices and the learning outcomes they are intended to measure, and highlights the need for alternative assessment models that emphasize process over product.
Md Zarzees Uddin Shah Chowdhury, Samin Khan· 0 citations
The AI-Enhanced Generative Learning in Mathematics (AI-GLM) framework addresses the representational, symbolic, and justificatory demands especially prominent in mathematics and yields propositions stated with the operationalizations and falsification conditions needed to test them.
AI should be understood as a mediating instrument, not as a more capable peer — a role that remains reserved for the teacher, who must mediate the very choice of the tool.
Heloísa Portugal, Carolina Ellwanger· Revista de Estudos Interdisc...· 0 citations
This study introduces the Physics AI-Replication framework, in which students identify challenging problems, use AI to explain underlying concepts and solutions, generate similar problems, and practice independently before reviewing answers.
This paper synthesizes peer-reviewed and carefully delimited contextual research on how generative AI use relates to undergraduate academic performance, with attention to what Canadian universities can already act on.
Pragalvha Sharma· Canadian Journal of Business...· 0 citations
It is suggested that structured and transparent AI integration, when aligned with instructional design principles, may support stronger performance outcomes than prohibition-based approaches within this course context.
Marine Aghekyan, Marie Botkin· Journal of Education and Pra...· 0 citations
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