Aug 2026· Educational Point· Vol 3, pp. e183· 0 citations· 22 references
TL;DR
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.
Abstract
As higher education faces the realities of technological advancements, institutions face the challenge of fostering high-level cognitive competencies while maintaining scalability. This paper proposes a holistic framework that integrates inquiry-based learning (IBL) with artificial intelligence (AI) to support learning. While traditional IBL is pedagogical and resource-intensive, the proposed model utilizes AI as a scaffolding layer, transitioning its use from an output generator to a metacognitive coach. Utilizing a conceptual framework methodology, the study maps the approaches between the stages of inquiry and AI interactions. In addition, the paper explores the institutional implications for teacher training and technological governance, arguing that the sustainability of such a system depends on shifting assessment from content mastery to measurable complex thinking skills. This contribution provides a cohesive foundation for administrators and educators seeking to implement active pedagogies in this era of AI.
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
This study investigates the effectiveness of integrating Artificial Intelligence (AI) within a Project-Based Inquiry (PBI) framework to enhance elementary students’ mathematical connections in geometry learning. AI functions as a cognitive scaffolding tool by providing adaptive prompts that support students’ reasoning, representation, and reflection, while PBI engages learners in authentic problem-solving tasks such as designing classroom layouts and park environments.The indicators of mathematical connections assessed in this study include: (1) connections between mathematical concepts (e.g., area and multiplication), (2) connections across multiple representations (visual, symbolic, and verbal), and (3) connections between mathematics and real-world contexts. This study employed a Design-Based Research (DBR) approach involving iterative cycles of design, implementation, evaluation, and refinement. Data were collected at the elementary school level through classroom observations, students’ project artifacts, short interviews, and AI–student interaction logs. The findings reveal substantial improvements in students’ mathematical communication and reasoning skills. Quantitative results indicate increases across all assessed dimensions, including precision of vocabulary usage (from 38% to 76%), logical structuring of arguments (from 42% to 71%), interactive discourse (from 36% to 68%), and accuracy in problem-solving (from 44% to 79).Qualitative findings further show that students transitioned from procedural to conceptual reasoning, demonstrating the ability to connect geometric concepts with real-world design contexts. AI-supported scaffolding facilitated this shift by guiding students from informal reasoning toward formal mathematical modeling. Interview data confirmed increased student engagement, metacognitive awareness, and confidence in problem-solving.These results suggest that AI-supported PBI effectively promotes interconnected mathematical understanding and meaningful learning experiences in elementary education.
F. Firdaus, Khairunnisa Zulfa Alifah· Frontiers in Education· 0 citations
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.
Lorna Uden, Gwo-jen Hwang· Journal of Computers in Educ...· 0 citations
One of the first comprehensive syntheses of AI integration across the entire CAR cycle is offered, linking it explicitly to critical-thinking development within a reflective, teacher-led inquiry framework, an intersection that remains underexplored in the extant literature.
Yusriani Yusriani, Andi Yuyung· ETDC: Indonesian Journal of...· 0 citations
A transformative journey from fear and resistance to collaborative innovation is revealed, demonstrating that AI integration, when scaffolded by inclusive design, ethical literacy, and practical application, can enhance faculty agency, curriculum responsiveness, and student engagement.
John C. Chick, L. Morello, Stephanie Staffey· International Journal for Ed...· 0 citations
Critical thinking (CT) and the limitations of Artificial Intelligence (AI)-based learning design have become major challenges in contemporary education. This study aims to identify students' critical-thinking challenges and to develop an AI-supported instructional framework based on the P3 Task Taxonomy. This study employed the ADDIE instructional design model and was limited to the Analysis and Design phases. The participants consisted of 42 pre-service physics teachers. Data were collected through open-ended CT tasks and analyzed descriptively based on students' cognitive response patterns. The findings revealed that 76.2% of the students predominantly exhibited System 1 thinking, characterized by intuitive responses with minimal elaboration, whereas only 20.3% demonstrated analytical System 2 reasoning. Based on these findings, an AI-supported P3 Task Taxonomy was developed that integrates Practice, Problem, and Project tasks with adaptive feedback and scaffolding mechanisms tailored to students' cognitive characteristics. Practically, the framework guides instructors in designing AI-assisted learning environments that foster CT development. Theoretically, this study extends current understanding of the relationships among cognitive systems, instructional task design, and AI support in the development of CT skills.
Nehru Nehru, M. Maison, K. Kamid et al.· Indonesian Journal of Scienc...· 0 citations