Special issue: Journal of Research on Technology in Education
Guest editors: Lorien S. Jordan, Bo Pei, Xin Qiao, and Jennifer Wolgemuth (University of South Florida)
Few developments have entered educational research with the speed and force of generative artificial intelligence (GAI). What began for many researchers as a set of optional tools for research assistance is becoming part of the conditions under which educational knowledge is designed, produced, analyzed, evaluated, and circulated (Roe, 2025). Across methodological traditions, GAIs growing presence in scholarly workflows is also changing how educational researchers think about expertise, authorship, and responsibility.
As GAI becomes embedded in educational inquiry, the field’s ethical frameworks and guidance have been slow to address the distinctive challenges artificial intelligence introduces (Ritzhaupt et al., 2026). Concerns such as transparency, accountability, bias, privacy, participant protection, and research integrity are increasingly acknowledged, but often in ways that leave their implications only partially explored (Marshall & Naff, 2024). Decisions about which tools to use, what data to share with them, how to report their use, and how much authority to grant their outputs shape the validity, integrity, and equity of the knowledge educational research produces (Hosseini et al., 2023; Levitt, 2026). As Ritzhaupt et al. (2026) observe, the field currently operates in a period of meaningful uncertainty, with limited consensus from professional associations, institutions, and publishers on what constitutes responsible practice. Sustained scholarly attention is therefore needed to examine the ethical dimensions of AI-assisted research across the full range of educational inquiry.
These ethical questions do not arise in identical ways across methodological traditions (Weber & Prietl, 2021). In quantitative research, GAI changes the roles of computational tools that has long structured statistical inquiry. Whereas conventional tools primarily execute researcher-specified procedures, GAI systems can generate code, suggest analyses, and interpret results. This shift raises ethical questions about analytic accountability, the reproducibility of computational workflows, and the validity of inferences drawn from reasoning that may not be fully verified (Blua et al., 2024; Resnik & Hosseini, 2025). In qualitative research, AI presses on longstanding assumptions about researcher judgment and interpretation, particularly in traditions where the researcher is understood as the primary instrument of inquiry (Brailas, 2025; Christou, 2023). Mixed methods research brings these concerns into relation (Costa et al., 2025) while also requiring scholars to navigate the integration of outputs produced through different epistemic and methodological assumptions (Bazeley, 2024).
Broader structural and societal concerns also shape the ethical use of AI in educational research. Many AI tools rely on training on data that disproportionately represent certain populations while underrepresenting others raising concerns, potentially embedding particular linguistic, cultural, and epistemological biases within these tools (Messeri & Crockett, 2024; Miragoli, 2025, 2025; Smirnova & Jordan, 2026). Ethical concerns also extend to the labor conditions behind these systems, the sovereignty of research data uploaded to commercial platforms, and the environmental costs of AI infrastructure, including energy and water use (Crawford, 2021; Muldoon & Wu, 2023; Ricaurte, 2022).
Taken together, these issues invite deeper scholarly attention to how ethics is being understood, justified, and enacted in AI-assisted educational research. This special issue is grounded in the recognition that the ethics of GAI use are complex and unsettled. What does ethics mean when researchers use GAI in educational research? What ethical principles, concerns, or frameworks are being invoked when scholars describe uses of GAI as responsible, appropriate, or acceptable? How are researchers making decisions about when to use GAI, for what purposes, and with(in) what limits? What forms of human and GAI interaction emerge across the research process, and how are those interactions shaping design decisions, interpretation, responsibility, and methodological practice?
Scope of the Special Issue: We invite conceptual, empirical, theoretical, and methodological contributions that deepen collective understanding of the ethical uses of GAI across the educational research process. We are especially interested in proposals that bring clarity, insight, and grounded reflection to questions of AI use in educational research. All submissions should explicitly name and describe the ethical framework, orientation, or tradition that grounds their discussion of GAI in educational research. For example, if a contribution focuses on issues of participant privacy when using GAI to analyze qualitative data, then the contribution should consider how privacy is defined and understood. Depending on the contribution’s orientation to ethics, privacy might be based on the Belmont Report’s ‘respect for persons’ principle, which emphasizes participants’ individual rights to control their personal information. In contrast, a contribution that draws on ethics derived from critical theories might conceptualize privacy as not just a matter of individual choice, but as concerning inequitable surveillance and data extraction – taking the position that the right to privacy standard is not universally applied.
We invite contributions such as, but not limited to:
- Case examples of ethical AI use in educational research
- Conceptual reflections and critical analyses of ethical AI use in educational research
- Ethical frameworks for understanding, evaluating, and guiding AI use in educational research
- Metaethical analyses of how ethics is defined, justified, and operationalized in discussions of AI use in educational research
- Empirical studies examining ethical questions, tensions, and practices related to AI use in educational research
- Policy analyses and recommendations for the ethical use of AI in educational research
Topics of interest include, but are not limited to, the following areas:
Research ethics, integrity, and responsibility
- Transparency and disclosure
- Plagiarism, attribution, and academic integrity
- Authorship and scholarly voice
- Human agency, oversight, and responsibility
Human–AI relations in educational research
- Changing relationships between researchers and AI systems
- Dehumanization and the displacement of human judgment
- The role of GenAI in shaping interpretation, design decisions, and scholarly practice
Data, privacy, and governance
- Data privacy and participant protection
- Surveillance and data extraction
- Data ownership, governance, and control
Justice, wellbeing, and human consequences
- Equitable access to AI
- Bias in training data and AI-generated outputs
- Health, wellbeing, and the human consequences of AI use
- Representation, marginalization, and harm in AI-mediated research
Environmental and infrastructural ethics
- Environmental ethics and the material costs of AI infrastructures
- Labor, extraction, and the conditions that sustain AI systems
Pedagogy and professional learning
- Pedagogical, curricular, and professional learning approaches for ethical AI use
Submission Instructions
Authors interested in contributing to this Special Issue should submit an abstract for consideration by November 15, 2026, using the following form: https://forms.gle/GUFrsMVFLCroPdkr5. Abstracts may be up to 1,000 words (excluding references) and must include a title page with the names and affiliations of all authors. Authors whose abstracts are invited for further consideration will be asked to submit a full manuscript by April 1, 2027. All Special Issue manuscripts will follow the JRTE author guidelines and undergo the standard JRTE peer review process.
JRTE author guidelines: https://www.tandfonline.com/action/authorSubmission?show=instructions&journalCode=ujrt20
Important dates
- August 1, 2026: Call for papers
- November 15, 2026: Deadline for abstract submissions: https://forms.gle/GUFrsMVFLCroPdkr5
- December 20, 2026: Notification of abstract review and invitation for full paper submissions
- April 1, 2027: Deadline for full paper submissions to JRTE’s online submission system
- June 1, 2027: Notification of first-round peer review results
- July 1, 2027: Deadline for first round revised submissions
- August 1, 2027: Notification of second-round peer review results (if required)
- August 31, 2027: Final revisions due
- October 1, 2027: Deadline for submission of final version of all accepted papers
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