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
Generative learning theory holds that learning depends on the learner’s own construction of relations between prior knowledge and new material, and its evidential practice has long inferred that construction from what the learner produces. External provision of such work is not new, but generative AI extends it across every generative strategy, supplies it on demand, and leaves little trace of its origin, so the locus of generative activity becomes instructionally and empirically ambiguous. Determining who performed the cognitive work that learning requires thus becomes a design and measurement problem. This paper examines that problem in mathematics learning. Through an integrative conceptual analysis of Fiorella and Mayer’s eight generative learning strategies, I identify four cross-cutting themes: the production-to-evaluation shift, metacognitive displacement, the scaffolding–substitution continuum, and reconfigured generative activity. Synthesizing these themes, I propose the AI-Enhanced Generative Learning in Mathematics (AI-GLM) framework, comprising (a) a typology of three AI roles, in which Generation Partner and Generation Substitute occupy the poles of a scaffolding–substitution continuum while Generation Catalyst is distinguished by a functional criterion rather than by a position on it; (b) a role determination mechanism specifying how tool design, task design, learner orientation, and teacher mediation combine to determine the role AI occupies in a given episode; and (c) five design principles. The 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.
This article examines the relationship between undergraduate students’ choice of generative artificial intelligence (AI) tools and the cognitive operations mobilized in scientific research, drawing on Vygotsky’s cultural-historical theory and the concept of the Zone of Proximal Development. It is based on an exploratory diagnostic study, with a mixed-methods approach, conducted with 166 undergraduate students at the Federal University of Mato Grosso do Sul, through a questionnaire with closed questions and open fields, treated by descriptive statistics and qualitative reading. The results indicate intensive use of AI — 72.3% of respondents use these tools weekly or daily — yet concentrated in general-purpose generative systems: among those who use AI in any research phase, 95.6% rely exclusively on generative or language-revision systems, with residual presence (2.4%) of AI-layered scientific databases. The open questions reveal concerns about reliability, plagiarism, impoverishment of one’s own learning, and the lack of institutional guidelines. The mismatch between intensity of use and functional adequacy of the tool is discussed as evidence of an unmediated zone of development, in which students build inadequate scaffolds on their own. The article concludes that 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. An analytical mediation matrix is proposed to guide this teaching practice.
Heloísa Portugal, Carolina Ellwanger· Revista de Estudos Interdisc...· 0 citations
The growing use of generative AI tools among students raises an important pedagogical question: how can AI be structured to promote active learning rather than passive answer-seeking? This study introduces the Physics AI-Replication (P-A.I.R.) framework, in which students identify challenging problems, use AI to explain underlying concepts and solutions, generate similar problems, and practice independently before reviewing answers. Survey data from 39 undergraduate students in algebra-based physics indicate that nearly all participants reported improved conceptual understanding following engagement with P-A.I.R. across the semester, and self-confidence scores were consistently above the scale midpoint (mean = 7.03; range 5-9). A strong association between conceptual clarity and replication helpfulness (Spearman r = 0.582, p<0.001) supports the framework's design. Qualitative findings highlight conceptual clarification and structured problem-solving. These results suggest that P-A.I.R. offers a practical approach for integrating AI into physics learning.
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
Purpose: The rapid expansion of generative artificial intelligence (AI) tools in higher education has prompted institutional responses ranging from prohibition to structured integration. However, empirical evidence comparing these approaches within authentic classroom contexts remains limited. This quasi-experimental study examines whether guided AI integration enhances student learning outcomes in a project-based undergraduate course.
Methodology: Two intact sections of a senior-level global trade course (N = 60) completed an identical semester-long group project. One section operated under a structured AI policy (Experimental Group), while the other followed a strict human-only policy (Control Group).
Findings: Independent-samples t-tests revealed statistically significant differences in overall project performance favoring the Experimental Group across written and presentation components. Students in the Experimental Group demonstrated stronger analytical organization, clearer conceptual application, and more effective synthesis of course content. Self-reported measures further indicated higher perceived learning, greater professional growth, and reduced stress. Indicators consistent with AI-assisted writing were observed in some Control Group submissions; however, these observations were not formally quantified and are interpreted cautiously.
Unique Contribution to Theory, Policy and Practice: Findings suggest 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. The study contributes empirical evidence to ongoing discussions regarding AI governance in higher education and offers implications for instructional policy design.
Marine Aghekyan, Marie Botkin· Journal of Education and Pra...· 0 citations
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