AI for Innovative Pedagogy, Educational Value, and Instructional Impact

A special issue of Education Sciences (ISSN 2227-7102). This special issue belongs to the section "Technology Enhanced Education".

Deadline for manuscript submissions: 1 September 2026 | Viewed by 1449

Special Issue Editor


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Guest Editor
School of Education, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA
Interests: artificial intelligence; generative artificial intelligence; AI agents; teaching; learning; instruction

Special Issue Information

Dear Colleagues,

This special edition focuses on the responsible adoption and use of artificial intelligence (AI) needed to support student learning in compulsory schools and institutions of higher education. As the global education community embarks on an era of AI, it is crucial that instructors and educational decision-makers balance critical, evidence-based approaches to AI with practical, replicable techniques for using it effectively in the classroom. With the rapid development and deployment of AI-powered educational tools, instructors must be critical in their selection of instruments and methods for integrating them into their instruction. For this to happen, instructors need fluency in AI literacy, clear information about which tools best meet their needs, and practical guidance for effective classroom implementation.

In response, this special edition solicits three broad themes for submissions:

  1. Advancing instructors’ AI literacy through sustained professional development programs offered across schools, universities, and organizations;
  2. Evaluative resources (e.g., frameworks and rubrics) and techniques to support educational stakeholders in assessing the instructional value of AI tools; and
  3. Instructional methods for using AI to enact experiential learning, project-based learning, personalized learning, simulations, and additional pedagogical techniques.

These themes represent broad areas that authors may address in their submissions. If authors have further ideas related to the special edition’s topics, they should kindly submit an inquiry. Lastly, for methodological approaches, submissions of case studies, content analyses, survey studies, and reviews of the literature resulting in practical tools and knowledge are preferred over studies focused on beliefs, attitudes, and acceptance of AI as an educational technology.

Dr. Todd Cherner
Guest Editor

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Keywords

  • artificial intelligence
  • AI
  • teaching
  • tool selection
  • pedagogy
  • assessment

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Published Papers (2 papers)

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Research

49 pages, 1958 KB  
Article
Introducing the Edu-GenAI Rubric: A Theory-Informed Tool for Assessing the Educational Value of Large Language Models and AI Media Generators
by Todd Cherner and Mags Donnelly
Educ. Sci. 2026, 16(5), 706; https://doi.org/10.3390/educsci16050706 - 30 Apr 2026
Viewed by 364
Abstract
The rapid proliferation of generative artificial intelligence (GenAI) tools has created an urgent need for instruments to evaluate their educational value as teachers, faculty, administrators, and instructional designers consider adopting them. While rubrics exist to assess mobile applications and virtual reality tools, no [...] Read more.
The rapid proliferation of generative artificial intelligence (GenAI) tools has created an urgent need for instruments to evaluate their educational value as teachers, faculty, administrators, and instructional designers consider adopting them. While rubrics exist to assess mobile applications and virtual reality tools, no comparable instrument has been developed specifically for large language models (LLMs) and AI media generators. The authors reviewed existing evaluation rubrics for edtech and GenAI tools, with edtech meaning digital tools that support ethical teaching to improve student learning and GenAI referring to neural networks that simulate human interactions by contextualizing relevant content based on learning needs. Grounded in Waks’ framework, the resulting Edu-GenAI Rubric comprises multiple dimensions organized into five domains: the Instrumental, Technical, Hedonic, Use, and Beneficial values. Dimensions include accuracy, productivity, personalization, citation, user interface, user experience, sharing, storage, and ethical dimensions encompassing data privacy, data transparency, guardrails, fair use, and algorithmic discrimination. The Edu-GenAI Rubric offers decision-makers with a preliminary, theory-informed instrument for evaluating GenAI tools in educational contexts that can be applied to institutional adoption decisions, developer benchmarking, and future research. Full article
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25 pages, 1802 KB  
Article
Integrating Generative AI and Cultural Storytelling to Enhance Geometry Learning in Vietnamese Primary Classrooms: A Quasi-Experimental Study
by Nguyen Huu Hau, Pham Sy Nam, Trinh Cong Son, Dao Chung Lan Anh, Nguyen Thuy Van, Pham Thi Thanh Tu, Tran Thuy Nga and Vo Xuan Mai
Educ. Sci. 2026, 16(4), 588; https://doi.org/10.3390/educsci16040588 - 7 Apr 2026
Viewed by 685
Abstract
In Vietnamese primary mathematics education, geometry instruction often emphasizes rote calculation and formula memorization rather than meaningful contextualization, leaving students disconnected from abstract concepts and lacking opportunities to connect learning with cultural identity. This quasi-experimental study investigates how integrating generative AI tools (ChatGPT, [...] Read more.
In Vietnamese primary mathematics education, geometry instruction often emphasizes rote calculation and formula memorization rather than meaningful contextualization, leaving students disconnected from abstract concepts and lacking opportunities to connect learning with cultural identity. This quasi-experimental study investigates how integrating generative AI tools (ChatGPT, DALL·E, Canva) with the culturally grounded Vietnamese folktale Bánh Chưng—Bánh Giầy can support Grade 5 students’ understanding of circle geometry. Employing a mixed-methods design with 30 students divided into experimental (AI + storytelling) and control (traditional instruction) groups, the study measured cognitive and affective learning outcomes through pre/post-tests, a validated 25-item questionnaire, interviews, and classroom observations. Quantitative results revealed significant improvements in the experimental group across all measured dimensions, learning interest, attentional focus, conceptual understanding, mathematics passion, and cultural preservation awareness, with large effect sizes. Qualitative findings confirmed enhanced engagement, multimodal conceptual clarity, and cultural affective resonance. The study demonstrates that low-cost, teacher-mediated generative AI can effectively support learning in resource-constrained primary settings when anchored in local narratives. Implications for ethical AI integration and teacher professional development in Vietnamese contexts are discussed. Full article
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