Multidimensional Responses to AI-Driven Transformation in Educational Contexts: Theoretical Frameworks, Tool Development, and Practical Exploration

The rapid transformation of educational contexts driven by artificial intelligence (AI), which covers primary schools, secondary institutions and universities, has reshaped the operational modes of educational systems, the delivery methods of teaching and the ways learners engage with knowledge. Contemporary educational environments are witnessing a growing influence of artificial intelligence-powered tools, spanning adaptive instructional platforms to data-informed learning support systems. Nevertheless, this transitional process also underscores a pivotal imbalance: educational institutions and researchers frequently prioritize the technical adaptation to artificial intelligence, while neglecting the psychological underpinnings that govern whether artificial intelligence-driven changes can yield effective, equitable, and sustainable learning outcomes. Additionally, there is an urgent requirement to formulate holistic responses to AI, encompassing theoretical frameworks that offer guidance for practical implementation, tools that are aligned with the learning needs of human beings, and practical strategies that provide support for diverse educators and learners. All these components must be grounded in an understanding of the ways in which AI interacts with human cognition, learning processes, motivation, and beliefs regarding learning. As such, researchers are invited to contribute original articles exploring multi-dimensional responses to AI in educational contexts, with explicit attention to the core principles of educational psychology (e.g., behaviorism, cognitive learning theory, constructivism, social learning theory) that underpin meaningful educational adaptation. In particular, cognition and metacognition emerge as key pillars for helping educators and learners navigate AI-driven changes, while considerations of motivation and individual beliefs ensure that responses to AI are inclusive and responsive to diverse needs.



We aim to explore and advance research as well as academic inquiry concerning multi-dimensional responses to artificial intelligence in education. Its focus is strictly directed toward one or more core principles of educational psychology, including human cognition, learning, motivation and beliefs about learning. The ultimate objective is to equip educators, researchers and policymakers with the capacity to effectively navigate and shape the educational changes driven by artificial intelligence. We are interested in studies that examine responses to AI in educational contexts through four critical lenses:

(1) theoretical/conceptual considerations (e.g., frameworks for aligning AI strategies with cognitive learning theory or constructivism)

(2) cognitive processes (e.g., how metacognition supports educators’ integration of AI tools or learners’ adaptation to AI-enhanced instruction),

(3) individual differences (e.g., how diverse learners’ motivation or resilience influences their response to AI)

(4) social interactions (e.g., how AI shapes teacher-student or peer dynamics in the context of adaptive responses to change).



We are interested in a broad set of detailed and in-depth papers, including empirical studies, methodological innovations, and systematic reviews or meta-analyses, as well as those that promote inclusive and equitable learning opportunities, enhance educational outcomes, and foster lifelong learning for all.

Note: The following vocational fields lie outside the scope of this call: digital education, entrepreneurship, higher education, language learning, and teacher training. Additionally, unless the core principles of educational psychology (noted above) are referenced, the following are also outside the scope of the call: AI (in isolation from psychological constructs), business, career readiness, digital adeptness, digital tech (focused solely on technical functionality), distance learning (without linking to cognition or motivation), elementary education (not grounded in educational psychology), engineering, entrepreneurial tendencies, healthcare and patient management, medicine, job satisfaction (without connecting to learning or instructional adaptation), marketing and branding, reading, pre-service perspectives (not tied to psychological principles), and sustainable development (in isolation from educational responses to AI).



We seek a broad set of papers: empirical, methodological, systematic reviews or meta-analysis, and papers on inclusive and equitable learning, enhanced educational outcomes, and lifelong learning. Topics include:

1. What do multi-dimensional responses to AI in educational contexts look like? (e.g., key qualities of theoretical frameworks, characteristics of learner-centered AI tools, strategies for practical implementation)? At different developmental stages (e.g., primary learners vs. adult lifelong learners)? From different perspectives (researcher, teacher, student, policymaker)? Across intersectional contexts (e.g., diverse cultural settings, inclusive environments for learners with disabilities)? Within communities that vary in AI acceptance?

2. Assessment/psychometrics: Measures and factors to assess the effectiveness of responses to AI in education (e.g., tools to evaluate how theoretical frameworks or AI tools influence cognitive engagement, motivation, or learning beliefs).

3. Associations with (positive) development: Extending earlier research to examine how responses to AI support educational quality—including links to improved learning outcomes, enhanced equity, and sustained engagement with AI tools.

4. Theorizing around responses to AI in education: Including reviews of important theoretical perspectives and how these frameworks inform the design of AI-responsive strategies.

5. Cognitive science and responses to AI in education: Exploring how cognitive processes (e.g., attention, memory, metacognition) shape effective adaptation to AI, and how AI tools can be designed to support these processes.

6. Empirical pieces with a wide array of methodological approaches, including quantitative (e.g., analyses of how AI tools influence motivation or cognitive outcomes), qualitative (e.g., explorations of educator beliefs about AI), and mixed methods.

7. Work that involves non-comparative, intersectional, and strengths-based approaches (e.g., centering the needs of marginalized learners in AI strategies), innovative and sophisticated statistical analyses (e.g., psychometric validation of AI-response assessment tools), and community-based participatory action research (e.g., collaborating with schools to co-design practical AI strategies).