AI evidence extraction

Ethical and Unethical Use of Generative Artificial Intelligence in Higher Education: Opportunities and Challenges for Online Graduate Education

Linda Cummins, Laurie Bedford, Dale Crowe · Journal of Online Graduate Education · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

9/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.65201/001c.161741

Methodology & findings

Study design

Narrative literature review synthesizing contemporary scholarship on Gen-AI in higher education; no primary empirical data collection or original experiments conducted.

Main result

The paper identifies that "Gen-AI offers diverse benefits to stakeholders in online graduate education" including "on-demand tutoring and feedback" for students and "improving productivity by generating course content" for instructors. However, "unethical use of Gen-AI can undermine academic integrity among students and faculty; Gen-AI course designs without human review and input can remove the human touch in education, limit student-faculty connection, and discourage student engagement." Additionally, research demonstrates that "the frequent use of Gen-AI writing tools is associated with greater procrastination, reduced retention of course material, and lower academic performance compared to peers who complete their own writing."

Reports effect sizes.

Research paradigm

Interpretivist/critical theory

Author conclusions

The authors conclude that "The rise of generative AI has reshaped the ethical landscape across all domains of education, but none more so than online graduate education, where face-to-face time with human instructors is limited or absent." They emphasize that "Higher education institutions must respond with integrated strategies that combine clear policies, adaptive teaching, and balanced practices, empowering students to engage responsibly with Gen-AI to maximize the benefits of authentic scholarly development, accuracy of writing content, and the development of their own human agency." They further stress that "Ethical and Unethical Use of Generative AI requires institutions to be intentional about which and how Gen-AI systems are adopted" and that universities are "morally bound to select AI tools with integrity."

Risk of bias

Publication bias (review limited to published literature); Selection bias in choice of cited studies; Potential author bias in framing Gen-AI as both opportunity and challenge; Selection bias in literature reviewed (emphasis on published studies may exclude institutional grey literature); Publication bias toward studies documenting problems rather than successful implementations; Geographic bias (examples focus on US context and English-language publications); Temporal bias (rapidly evolving field means some citations may be outdated); Selection bias: narrative review methodology not based on systematic search strategy; Publication bias: likely emphasizes published literature over gray literature; Recency bias: rapid evolution of Gen-AI tools may make literature quickly dated; Geographic bias: emphasis on US higher education context; Language bias: English-language sources prioritized

Limitations

The authors note that "very little has been written in a substantive way pertaining to the use and misuse of AI for culminating projects" and that "there appears to be a gap in the use of AI for other types of culminating projects, such as capstone projects, master's theses, etc." They further identify that "In the realm of doctoral dissertations and theses research is limited and still evolving." The review also acknowledges that "detection challenges are currently complex, and there are some problems with accuracy" regarding AI-generated ghostwriting detection, as "AI-generated text is novel and context-specific, which makes detection difficult."

Open questions raised

Substantive research on AI use and misuse in culminating projects (capstones, master's theses, dissertations)
Qualitative, quantitative, and mixed methods studies on Gen-AI ethics in graduate culminating projects
Institutional policies for ethical Gen-AI use and their effectiveness
Studies examining student motivations and pressures in online dissertation environments related to ghostwriting and contract cheating
Research on how autonomy and human agency can be preserved as Gen-AI assumes more instructional roles
Substantive research on the use and misuse of AI for culminating projects is lacking

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