Teaching and Assessing with AI: Teaching Ideas, Research, and Reflections

By the end of 2022, generative AI technologies based on large language models had become easily accessible and increasingly widespread. As disruptive technologies, their full impact and range of applications are still unfolding. Education is one of the six key application areas for generative AI identified by Chiarello et al. (2024), alongside human resources, programming, office automation, social media, and search engines. In education, generative AI offers new possibilities for teaching and assessment, such as personalized tutoring, automated feedback, and adaptive systems (e.g., Jensen et al., 2024). However, these innovations also present significant challenges that educators must navigate to ensure that all learners may benefit from them. Specifically, educators are called to tackle, among other things, the changing dynamics of teacher-student communication, the shifting nature of their role, ethical concerns in assessment, and the impact of generative AI on intercultural understanding in increasingly diverse education settings.

The aim of this Research Topic is to offer a platform for researchers and educators to reflect on the impact of these technological shifts on classroom culture and to share innovative pedagogical experiments that enhance teaching and assessing practices. The editorial team has chosen a ‘short-paper’ format to capture fresh ideas, conversations, and pedagogical experimentations, making them easily accessible to a wider audience of researchers and teachers.

This multidisciplinary project welcomes contributions from teachers and researchers across a variety of fields (e.g., communication, education, computer science, economics, mathematics, and biology). We are particularly interested in practices with cross-disciplinary relevance that can shape classroom cultures at the tertiary level. The generative AI tools discussed in contributions must be available in a free or free-tier version, or commonly available as part of university-wide enterprise licenses, or similar.  

Submissions may focus on, but are not limited to, the following themes:  

• effective practices and/or lessons learned in integrating generative AI tools in classroom settings
• successful strategies for supporting teaching, learning, and assessment with generative AI tools
• real-time student feedback through AI-driven feedback mechanisms 
• critical discussions on academic integrity, critical thinking, authorship, and other ethical concerns related to generative AI in classroom teaching, assessment, and curriculum design
• advances in instructional communication and/or pedagogical theories in the light of the integration of generative AI tools in classroom settings 
• student engagement, participation, and collaborative learning in the AI-mediated classroom
• educational materials, instructional sources, and teacher professional development in the AI-mediated classroom
• development of knowledge and skills needed for learning in the generative AI era (e.g., digital literacy and critical generative AI literacy)
• critical discussions on the challenges that generative AI poses to classroom culture (e.g., digital divide, power dynamics, and intercultural inequalities)
• the sustainability of generative AI tools in classroom settings.

The Topic Editors seek three types of submissions: 

1. Great Ideas for Teaching/Assessing with AI (GIFT-AIs)

GIFT-AIs are instructor- and student-tested ideas for effectively teaching and assessing a wide range of topics using generative AI. The goal of GIFT-AIs is to inspire educators to include and to experiment with AI in their teaching practices. A GIFT-AI may include:

a) original, single teaching activities; b) original teaching units spanning several days or weeks; c) original semester-long projects or approaches for an entire course; d) systematic, data-driven reflection on assessment practices to monitor and support student learning and improve the quality of specific courses or overall programs.   

GIFT-AI submissions should include the following components:

1. title (begin with 'GIFT-AI:')
2. intended course (subject, class size, level, modality, duration, recommended skill sets for students, weekly schedule, cross-disciplinary relevance, and other relevant information)
3. objectives/learning outcomes
4. theoretical rationale (gamification, authentic assessment, collaborative learning, etc.)
5. step-by-step implementation instructions (preparation/preliminary steps, required learning materials, etc.)
6. debriefing, appraisal (including any limitations and/or suggested variations), and reflections
7. references
8. appendix (optional).

The journals involved in this Research Topic accept a wide range of article types. Suitable article types for GIFT-AI submissions may include Brief Research Report and Technology and Code.

2. Short Research Reports on Teaching/Assessing with AI (RESEARCH-AIs)

RESEARCH-AIs offer researchers engaged in advancing our understanding of the use of AI in educational settings the opportunity to present recent findings from a series of interconnected studies and classroom-based research agendas. The goal of RESEARCH-AIs is to inform the scholarly community about comprehensive research programs and current research developments, propose future research directions, and contribute to evidence-based practices for integrating AI in teaching and assessment. Submissions to RESEARCH-AIs should highlight how the presented findings stem from a coherent body of research that collectively addresses a unifying research question or theme.

RESEARCH-AI submissions should include the following components: 

1. title (begin with 'RESEARCH-AI:')
2. abstract (100–200 words)
3. introduction (contextualization of the paper)
4. main text (comprehensive outline of the research agenda, with discussion of relevant studies, theories, or models, including research methodologies implemented and key findings)
5. conclusion (summarizing key points, highlighting the research agenda’s contributions, and suggesting future directions)
6. references
7. appendix (optional).

The journals involved in this Research Topic accept a wide range of article types. Suitable article types for RESEARCH-AI submissions may include Brief Research Report and Technology and Code.

3. Reflections on Teaching/Assessing with AI (REFLECTION-AIs)

REFLECTION-AIs are reflective pieces exploring the possibility of AI in teaching and assessing practices. They are intended to inspire meaningful, thought-provoking debates among educators, also in the style of op-ed. REFLECTION-AIs should be forward-looking, outlining potential trajectories in pedagogical practices, and/or provocative, discussing critical issues. These reflective pieces may be based on a variety of approaches (including, for instance, autoethnography, case studies, and historical analysis) and authors may include bold suggestions and proposals. Reflective pieces that critically question the inclusion of generative-AI technology in the classroom are welcomed. REFLECTION-AI submissions may be written in first person form, if relevant, and should maintain a clear focus.  

REFLECTION-AI submissions should include the following components: 

1. title (begin with 'REFLECTION-AI:')
2. abstract (100–200 words). 
3. introduction (contextualizing the reflection and presenting the specific focus guiding the reflection) 
4. main argument (discussion of the central theme, with relevant examples, case studies, or personal teaching experiences, if applicable. May include an outline of possible future trajectories, critical reflections, bold suggestions, and innovative ideas). 
5. conclusion 
6. references 
7. appendix (optional). 

The journals involved in this Research Topic accept a wide range of article types. Suitable article types for REFLECTION-AI submissions may include Perspective and Opinion.

Word Count and Supplementary Material

Submissions should be a maximum of 3,000 words, excluding the abstract, section titles, figure and table captions, funding statement, acknowledgments, and bibliography references. The word count includes appendixes, unless the selected article type allows more than 3,000 words.

Relevant generative AI codes and scripts mentioned in the text, as well as data that are not of primary importance to the text, or which cannot be included in the article because they are too large or the current format does not permit it (such as videos, raw data traces, PowerPoint presentations, etc.), can be uploaded as supplementary material during the submission procedure and will be displayed along with the published article. For more information about the submission of supplementary material, please see here.

Manuscript Summaries

Authors are encouraged to submit a manuscript summary before submitting a full manuscript. The deadline for manuscript summaries is 31/01/2025. A manuscript summary is simply a summary of the manuscript one plans to submit. The Topic Editors will review all manuscript summaries and provide feedback that authors should consider when writing their full manuscript. Manuscript summaries will not be published externally and there is no associated fee. The maximum word count for manuscript summaries is 2000 words.

Formatting Style 

Harvard is Frontiers’ recommended but not mandated citation style. For manuscripts reviewed and accepted for publication, the production process can include changing the style to Harvard.

Keywords: artificial intelligence (AI), AI in education (AIED), AI-driven pedagogy, teaching and assessment, instructional communication, classroom culture

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.