Call for Abstracts for 2025 Special Issue: Practical uses of Generative AI

The Journal of Teaching and Learning with Technology (JoTLT) invites proposals for articles about the practical uses of generative AI in enhancing teaching and learning in higher education. We seek proposals that focus on the practical applications of generative AI in higher education, backed by data and tied to pedagogical theory/frameworks.  

Submissions should provide detailed accounts of implemented AI-driven initiatives, highlighting their impact on teaching and learning. We aim to advance our understanding of the effects of teacher or learner use of Generative AI in teaching and learning. 

Generative AI has shown potential to transform teaching practices by helping educators offer personalized learning experiences, facilitate collaborative learning, and create engaging, interactive, and accessible learning materials. Additionally, by streamlining administrative tasks and improving efficiency, generative AI may enable educators to focus more on experiential, engaged, and high-impact teaching practices. Learners also benefit from faculty including generative AI in their teaching, as it helps develop modern digital skills and career competencies. 

We invite 300- to 500-word abstracts submitted via this form by August 25, 2024, for data-driven articles, case studies, or critiques. Abstracts will be anonymously reviewed, and invitations to submit a full article will be sent the week of September 2, 2024. The full article will be due June 1, 2025, followed by anonymous review. The target date for publication is December 2025. 

Manuscript categories are described below; please list the category of your submission so we know how to review it. 

• Articles: Data-driven formal research projects with appropriate analysis. These studies, either quantitative or qualitative, should establish research rigor leading to significant new understanding in pedagogy.  

• Case Studies: Intense analysis of specific teaching situations or problems that led to solutions. Case studies should include description of the teaching situation or problem, solution(s) attempted, quantitative or qualitative analysis of the effectiveness, reflection on implications, and possible generalization to other settings or populations. 

• Critiques: A systematic and detailed assessment of a published manuscript related to teaching and learning in higher education. A critical evaluation should deconstruct the work, presenting details and analysis of the considered work.   

For more information or questions, please contact the journal’s Editorial Team: Michael Morrone (Co-Editor in Chief) or Adam Maksl (Guest Editor) at facet@iu.edu.