Individuals who study and use artificial intelligence (AI) have a unique vantage point navigating -- and redefining -- a world including educational institutions (i.e., primary and secondary education, universities) that reinforce and privilege those who are technology orientated. In addition, a need exists for relevant employment and entrepreneurial skills, quality learning environments, qualified teachers, and digital literacy skills to find, evaluate, manage, create, and communicate information using artificial intelligence. As such, researchers are invited to contribute original articles exploring AI in education considering human cognition, learning, motivation, and beliefs about learning (computers, multimedia, etc.). In particular, cognition and metacognition help people to adapt and thrive in the face of AI. Theoretical considerations showcase the dynamics underlying new AI educational worlds. (e.g., behaviorism, cognitive learning theory, constructivism, identity behavior theory, and social learning theory).

We aim to explore and advance research and scholarship on AI related to one or more of the core principles of educational psychology to empower more individuals in education to effectively utilize AI: human cognition, learning, motivation, and beliefs about learning. We are interested in studies that examine AI in education and (1) theoretical/conceptual considerations, (2) cognitive processes, (3) individual differences, and (4) social interactions. We are interested in a broad set of detailed and in-depth papers, including empirical, methodological, and systematic reviews or meta-analysis, as well as those that promote inclusive and equitable learning opportunities, enhancing educational outcomes, and fostering 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, business, career readiness, digital adeptness, digital tech, distance learning, elementary education, engineering, entrepreneurial tendencies, healthcare and patient management, medicine, job satisfaction, marketing and branding, reading, pre-service perspectives, sustainable development.

The submissions should be detailed and in-depth considering education and artificial intelligence (AI) and must consider one of more of the core principles of educational psychology: human cognition, learning, motivation, and beliefs about learning.

Note: The following vocation 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, business, career readiness, digital adeptness, digital tech, distance learning, elementary education, engineering, entrepreneurial tendencies, healthcare and patient management, medicine, job satisfaction, marketing and branding, reading, pre-service perspectives, sustainable development.

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 AI learning communities in education look like? (qualities, characteristics, strategies)? At different developmental stages? From different perspectives (researcher, teacher, student)? Across intersectional contexts? Within AI accepting cultures?

2. Assessment/psychometrics: Measures and factors to assess the advancement of AI in education.

3. Associations with (positive) development, extending earlier and examining AI educational quality.

4. Theorizing around AI in education including reviews of important theoretical perspectives by David Berliner, Paul Pintrich, Donald Super, Carl Rogers, Roger Säljö, and Alan Lesgold.

5. Cognitive science and AI in education.

6. Empirical pieces with a wide array of methodological approaches, including quantitative, qualitative, mixed, etc.

7. Work that involves non-comparative, intersectional, and strengths-based approaches, innovative and sophisticated statistical analyses, and community-based participatory action research.

Are you interested in contributing a manuscript to this Research Topic?

Creating Powerful AI Learning Environments to Foster Effective Learning and Instruction for All