Ethics, privacy, bias, over-reliance, accuracy, usability and cost are key challenges AI use poses for learner satisfaction and engagement¶

Claim · All claims
Evidence · 1 study (2 entries) · quant-synthesis r2 · q2
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 2 of 2 entries) · not reviewed by a person

Subclaims¶

Reading the evidence codes

q is the design tier, i the impact, n the sample, and the kind with its r the rigour judged within that kind: what each code means.

This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.

q2 i? Unaddressed risks around privacy, bias, and misuse may lead learners to hesitate to use AI tools, lowering satisfaction and engagement. → Ana Katalinic 2026 q2 i? AI hallucination and misclassification (e.g., engagement detection, performance prediction) can negatively affect learner satisfaction and engagement. → Ana Katalinic 2026 (2)

Evidence¶

Ana Katalinic 2026¶

Ana Katalinic, Vanja Slavuj, Danijela Jaksic. (2026). Artificial Intelligence in Online Education: A Systematic Review of Its Impact on Learner Engagement and Satisfaction. Educ. Sci. 2026, 16, 389. https://doi.org/10.3390/educsci16030389

q2 · i? · review · r2

In its RQ2 synthesis of the 30 included studies, the review identifies seven challenge factors (ethics/privacy, over-reliance, bias, accuracy, motivation impact, usability, cost) and warns that unaddressed risks may lower satisfaction and engagement.

"If these risks are not properly addressed, learners may hesitate to use AI tools, potentially lowering their satisfaction and engagement levels."

Ana Katalinic 2026 (2)¶

q2 · i? · quant-synthesis · r2

The review reports, citing Bagunaid et al. (2022), that AI prediction and recommendation systems can misclassify performance and give suboptimal guidance. It also cites Sasaki et al. (2023) on engagement/sentiment misclassification in small samples.

"AI-based performance prediction and recommendation systems can fail to accurately evaluate individual student progress, with slow processing and misclassification of performance levels leading to suboptimal guidance and negatively affecting learner satisfaction and engagement"

Discussion¶

GenAI raises privacy, transparency, bias and accuracy concerns in group work, though empirical evidence remains limited — related
Algorithmic bias and data privacy are central ethical risks of AI in adult education — related
Nursing students and faculty report concerns that AI use may weaken critical thinking, enable plagiarism and misinformation, and create unequal access — related
Students' reservations about AI feedback centre on trustworthiness (data privacy) and goodwill (impersonal, demotivating feedback) — related
Students raised four categories of concern about potential misuse of MMLA data: judgment, privacy, career impact, and academic integrity — related
Human-AI relationship concerns persist across all CAI generations while academic integrity and data privacy are emerging ethical concerns — related
Limited output accuracy and over-reliance are the most pressing challenges Chinese HSS students report in GenAI use, alongside strong ethical concern — related
Only a small fraction of AIEd research addresses ethical implications, challenges and risks — related
GenAI in education presents a dual profile of potential benefits and significant concerns — related
Common risks of AI integration in nursing education include technological inequities, faculty preparedness gaps, and ethical concerns around privacy and bias — related
AI in education poses challenges across three interrelated domains: knowledge itself, the process of knowing, and the socio-environmental impact of knowledge production — related
Interaction and usability challenges affect 85.3% of reviewed CAI articles, with usability problems most observable in mature rule-based agents — related
Arts-based methods in community-based research raise specific ethical challenges around anonymity, ownership, representation and emotions, alongside practical limits of time, training and cost — related

Page metadata

Field	Value
`type`	`claim`
`id`	`ai-challenges-engagement-satisfaction`
`title`	`Ethics, privacy, bias, over-reliance, accuracy, usability and cost are key challenges AI use poses for learner satisfaction and engagement`
`description`	`Ethics, privacy, bias, over-reliance, accuracy, usability and cost are key challenges AI use poses for learner satisfaction and engagement`
`status`	`review`
`evidence_strength`	`weak`
`newest_evidence`	`2026`
`source_checked`	`2026-10-20`
`generated`	by `process:wiki-ingest` on `2026-10-12`
`trust`	unverified — no one has confirmed this page's content
`sources`	2 — `ana-katalinic-2026`, `ana-katalinic-2026-2`

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