PurposeThis paper proposes a pedagogical framework for cultivating inquiry-driven agency in student engagement with artificial intelligence (AI). It addresses a gap in the AI-in-education literature, in which substantial scholarly attention to material access and algorithmic fairness has been accompanied by comparatively less sustained attention to the learner-side capacities that determine whether AI is engaged with critically or passively. The paper offers three connected contributions: a cyclical model of inquiry-driven learning, a four-phase curricular sequence for student-AI engagement, and an operational assessment counterpart that remains diagnostically meaningful in AI-mediated environments.Design/methodology/approachThe paper employs an integrative conceptual approach in the tradition of model-paper scholarship (Jaakkola, 2020). It synthesises five bodies of literature: AI in education and AI literacy frameworks, critical scholarship on educational technology, foundational work on curiosity, dual-process cognition, and adaptive expertise, constructive-developmental theory, and research on question-asking in learning and authentic assessment. Sources were identified through targeted citation tracing supplemented by database searches. Theoretical resources are justified at the point of use rather than methodologically up front.FindingsThe analysis demonstrates that material access and algorithmic fairness, while necessary, do not on their own account for the learner-side capacities that determine whether AI is engaged with critically or passively. It argues that inquiry-driven agency, the capacity to ask productive questions of AI rather than receive its answers, is a worthwhile object of pedagogical design. The Student-AI Learning Trajectory Model presents this development as a four-phase curricular sequence (AI as Tool, Machine, Object, and Partner), with progression understood as a curricular design decision made by educators rather than a developmental trigger inside the learner.Research limitations/implicationsThe model is conceptual rather than empirically validated, and its applicability to younger learners, neurodiverse learners, and low-literacy contexts has not been fully worked out. The paper signposts several lines of empirical inquiry: longitudinal studies of SALT-informed curricula, methodological development of inquiry-sequence assessment instruments drawing on the Objective Structured Clinical Examination tradition, and comparative studies across institutional contexts and learner populations. The contribution is bounded to the pedagogical dimension and does not address the structural reforms that critical scholarship has identified as necessary for genuine educational equity.Practical implicationsThe paper has implications for curriculum design, educator development, and assessment practice. Curricula can sequence student-AI engagement across phases of increasing sophistication rather than treating AI literacy as content to be covered. Educator development requires sustained investment in pedagogical capacity, particularly the modelling of inquiry-driven engagement in front of students. Institutions should treat the redesign of assessment for the AI era as a pedagogical question rather than an integrity question, with inquiry-sequence assessment offering one operational direction. Equity-oriented investment should prioritise educator capacity over the mass distribution of adaptive learning software.Social implicationsThe cultivation of inquiry capacity in AI-mediated learning environments matters for the kinds of learners that schools and universities send into a world increasingly shaped by automated systems. Without deliberate pedagogical design, AI risks becoming a multiplier of existing educational divides rather than a neutralising force. The model locates responsibility with institutions and educators designing learning environments, not with individual students, and identifies the risk of a pedagogical Matthew Effect in which inquiry-driven pedagogy becomes available primarily to already advantaged students absent institutional investment in educator capacity.Originality/valueThe paper brings together three conversations that have developed largely in parallel: AI literacy as a set of competencies, evaluative judgement as a response to generative AI's destabilisation of assessment, and curiosity as a driver of self-directed learning. Its distinctive contribution is the articulation of phase progression as a curricular sequence informed by a cyclical model of inquiry learning, complemented by an operational assessment counterpart. The paper offers vocabulary and tools for educators, curriculum designers, and assessment scholars responding to the question of how AI should be integrated into the work of cultivating independent learners.
Blended Learning (BL) has become a widely adopted educational model, merging traditional face-to-face instruction with online learning tools and techniques. It offers many advantages, including flexibility and structured interaction. During the pandemic and post-pandemic era, there is a requirement to deliver robust, adaptable, and context-sensitive instructional design frameworks that will lead to a paradigm shift in the Teaching and Learning System (TLS). Despite the availability of established models like Analysis, Design, Development, Implementation, and Evaluation (ADDIE) and conceptual frameworks like Technological Pedagogical Content Knowledge (TPACK), there may be substantial research gaps available to fully support the dynamic demands of BL approaches. This research proposes a comprehensive instructional design framework integrating the existing available standards and models for enhanced learning experience, promoting motivation, engagement, and reflection among learners. The framework envisions a modular digital platform where TPACK informs the design phase, and Self-Regulated Learning (SRL) strategies are embedded throughout development, implementation, and evaluation. This proposed model, currently in the early stage of development, aims to empower instructors to design more coherent, adaptive, and learner- centered BL experiences.
The article is devoted to the psychological and pedagogical principles of medical professionals training in the conditions of transformational changes using digital technologies. The purpose of the study is to determine the conceptual principles of the efficient integration of digital tools into medical education to increase the level of clinical, analytical and communicative competencies formation, as well as to develop recommendations for the optimal use of these technologies in the educational process. Analysis of modern research, regarding the application of digital technologies in medical education, in particular, main trends, achievements and problems was conducted. The study also reveals psychological and pedagogical mechanisms for the effective implementation of digital tools, contributing to the harmonious combination of practical training and the development of students’ personal qualities. It was established that digital technologies allow adapting the educational process to the needs of each student, ensure safe and effective development of practical skills, stimulate critical thinking, responsibility and professional self-improvement. The integration of digital technologies into medical education is efficient tool for transforming the educational environment and preparing future medical professionals for the challenges of the modern healthcare system.
We investigate student housing in Koblenz through two lenses, affordability (rent, €/m2, rent-burden) and accessibility (distance to campus, commute time, proximity to public transport), by integrating survey microdata with geospatial indicators and density-based clustering. Regression analyses show absolute rent is primarily driven by room size, while €/m2 is higher for smaller units and, on average, for international students, who also face greater odds of high rent burden. Commute time scales with distance and exhibits no status effect once distance is controlled; however, international students attend campus fewer days per week and are more frequently located in peripheral or weaker-connected areas. Perception evidence aligns with these outcomes: international students prefer to live closer and expect larger benefits from proximity. Findings imply that who pays and who reaches diverge systematically; policy should moderate small-unit price premiums and prioritize well-connected placements to align preferences with assignments and reduce participation gaps.
Automatic Question Generation (AQG) for adaptive learning is an emerging field focused on creating high-quality, contextually relevant questions to support personalized assessment and learning pathways. Generating meaningful, conceptually accurate, and appropriately challenging questions tailored to diverse learners is often time-consuming and inconsistent. This study addresses this challenge by designing an adaptive AQG system that leverages Bayesian Knowledge Tracing (BKT) and Large Language Models (LLMs) to generate personalized formative assessment questions based on a student’s current mastery level. The proposed system utilizes students’ quiz performance data to predict concept-wise mastery through BKT, and then prompts the LLM to generate questions aligned with the predicted knowledge state. While the system successfully generates relevant questions, initial findings reveal limitations in generating diverse questions. Most questions were factual and recall-based, focusing on lower-order Bloom’s Taxonomy levels, with minimal to no variation across students. There was also lack in adjusting question diversity to individual mastery levels. This study contributes to the growing literature on personalized learning by integrating knowledge tracing with LLM-driven question generation. Future enhancements will aim to address the current limitations by incorporating Bloom’s Taxonomy alignment, student preferences, and prior learning history. Additionally, expanding the dataset with timestamped interactions will enable modeling of learning trajectories over time, allowing for more refined and adaptive assessment. By improving both the quality and personalization of generated questions, this approach has the potential to significantly enhance student engagement and learning outcomes in adaptive learning environments.
