Jul 2026· Journal of Computers in Education· 0 citations· 18 references
TL;DR
By integrating neuroscience-informed learning design, PBL pedagogy, and responsible AI use, the LEARN framework contributes a theoretically grounded model for redesigning assessment practices that sustain deep, self-directed, and reflective learning in the context of generative AI.
Abstract
In an era of rapid generative artificial intelligence (GAI) integration into education, students are increasingly using these tools not merely as learning aids but as their primary means for completing assessments. This shift raises significant concerns regarding academic integrity, cognitive offloading, and the erosion of critical thinking. To address these challenges, this paper advances a conceptual, neuroscience-informed framework, the Lifelong Learning, Engagement, Active Processing, Reflection, and Neuro-based Design (LEARN) model, for the ethical and pedagogically grounded integration of GAI into assessment contexts. Grounded in over two decades of experience with problem-based learning (PBL), the framework emphasises learner autonomy, adaptability, and sustained cognitive engagement. The LEARN framework synthesises principles from cognitive and educational neuroscience with constructivist learning theory to explain how learning processes such as neuroplasticity, effortful cognition, metacognitive regulation, and socio-emotional engagement can be intentionally supported in AI-mediated environments. Each component positions GAI as a cognitive scaffold rather than as a cognitive substitute, encouraging critical evaluation, reflective judgement, and ethical self-regulation. By integrating neuroscience-informed learning design, PBL pedagogy, and responsible AI use, the LEARN framework contributes a theoretically grounded model for redesigning assessment practices that sustain deep, self-directed, and reflective learning in the context of generative AI.
Generative artificial intelligence (GenAI) has entered education faster than the theoretical and methodological frameworks used to evaluate it. This critical integrative review asks a more consequential question than whether GenAI ‘works’: under what pedagogical conditions can it augment learning without displacing learner agency, epistemic responsibility, and higher-order cognition? A purposive corpus of 56 distinct sources was synthesized, spanning foundational theories of experiential, sociocultural, situated, distributed, social-cognitive, cognitive-load, self-regulated, and technology-mediated learning; established technology-acceptance models; artificial-intelligence-in-education and AI-literacy frameworks; policy guidance; and empirical, systematic-review, and meta-analytic evidence published through 2026. The synthesis indicates that GenAI is most defensibly conceptualized as a fallible cognitive artifact embedded in a distributed learning system rather than as an autonomous epistemic authority. Positive effects on achievement, motivation, engagement, creativity, and higher-order thinking are increasingly supported, but they are conditional on task design, scaffolding, interaction quality, verification practices, AI literacy, and sustained human oversight. Evidence for metacognitive improvement remains weaker, while unstructured use can promote cognitive offloading, overconfidence, dependency, and integrity risks. To integrate these findings, this article proposes the Human–AI Pedagogical Agency Framework (HAPAF), composed of five interacting layers: epistemic positioning, learner agency, interaction design, verification, and governance. The framework reframes effective GenAI integration as an agency-preserving pedagogical design problem rather than a tool-adoption problem.
A. Haro-Sarango· Multidisciplinary Latin Amer...· 0 citations
A conceptual framework offering AI designers, teacher educators, and policymakers a psychologically grounded, empirically mapped, and internationally contextualised basis for developing AI-supported educational systems that advance equity, learner engagement, and meaningful educational transformation is proposed.
Arpana Koul· Review of Artificial Intelli...· 0 citations
The rapid integration of artificial intelligence (AI) into higher education has transformed how students access information, engage with learning tasks, and construct knowledge. While previous studies have primarily focused on the adoption, effectiveness, and ethical implications of AI-assisted learning, limited attention has been given to how AI influences students’ identities as learners. This study explores how university students construct their learner identities in the era of artificial intelligence. Employing a qualitative phenomenological design, the study involved six higher education students who regularly used generative AI technologies to support their academic learning. Data were collected through semi-structured interviews and a focus group discussion and analyzed using inductive thematic analysis. The findings revealed four interconnected themes: becoming an AI-enhanced learner, negotiating independence and dependence in learning, redefining competence and expertise, and envisioning future-ready learner identities. Participants perceived AI as a learning companion that enhanced their capabilities while simultaneously requiring them to negotiate issues of autonomy, responsibility, and authenticity. The findings further indicated that students increasingly defined competence in terms of critical evaluation and effective human-AI collaboration rather than information acquisition alone. Moreover, participants’ engagement with AI was strongly connected to their aspirations for future academic and professional participation. The study contributes to the growing literature on AI in higher education by highlighting the identity-related dimensions of AI-mediated learning and offering insights into what it means to be a learner in an increasingly AI-driven educational landscape.
R. Venketsamy, Naelul Rohmah· DUTIES: Education and Humani...· 0 citations
A holistic framework that integrates inquiry-based learning (IBL) with artificial intelligence (AI) to support learning is proposed, arguing that the sustainability of such a system depends on shifting assessment from content mastery to measurable complex thinking skills.
Lori B. Doyle, Jill L. Swisher· Educational Point· 0 citations
The rapid development of artificial intelligence (AI) has significantly transformed educational practices, especially in classroom learning environments. While AI provides opportunities for personalized learning, adaptive feedback, and enhanced engagement, concerns have also emerged regarding student dependency and reduced critical thinking skills. This study aims to explore the integration of AI in classroom learning and its relationship with growth mindset development among students. Using a qualitative descriptive approach, the research analyzes classroom learning strategies that utilize AI as a collaborative learning partner rather than as a replacement for human cognition. Data were obtained from literature studies and classroom learning observations focusing on AI-supported learning activities, critical thinking development, and growth mindset orientation. The findings indicate that AI can function effectively as a learning stimulus that encourages reflective thinking, supports student engagement, and facilitates feedback-driven learning processes. However, without proper guidance, AI may also lead to overreliance and reduced independent thinking. Therefore, teachers play a crucial role in guiding students to use AI responsibly and productively. The results show that AI integration combined with growth mindset principles can enhance learning motivation, promote resilience when facing academic challenges, and encourage collaborative knowledge construction. This research highlights the importance of pedagogical strategies that position AI as a learning partner while maintaining the central role of human reasoning and creativity in education.
Mita Ariyanti, Mustika Ayu, Zulia Maharani et al.· Buletin Edukasi Indonesia· 0 citations
The research outcomes demonstrate that AIAS functions effectively as a learning architecture, aligning academic integrity with instructional design, and offers a replicable model for fashion programs and other disciplines seeking responsible AI integration.
D. Shen· PUPIL International Journal...· 0 citations
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