Students perceive LLM chatbots as useful for learning but requiring critical use, reporting hallucinations, overconfidence, and dependency risks¶
Claim · All claims
Evidence · 1 study · associationalr1·q1
Currency · newest study 2026 · checked against its sources 2026-10-20 (automated, 1 of 1 entry) · 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.
q1 i? Questionnaire responses across both user-study scenarios show students see utility in LLM output but do not unconditionally trust it, and open feedback names hallucinations, overconfidence, and inconsistencies as problems. → Markos Stamatakis 2026
Evidence¶
Markos Stamatakis 2026¶
Markos Stamatakis, Omkar Gavali, Joshua Berger, Christian Wartena, Anett Hoppe, and Ralph Ewerth. (2026). Are LLM-based Chatbots Good Enough to Support Computer Science Students in Multiple-Choice Exercises? https://arxiv.org/abs/2606.15919 doi:10.48550/arxiv.2606.15919
q1 · i? · associational · r1
Questionnaire with five-point Likert scales and open-text feedback in both user-study scenarios (Sec. 5). The article reports that "students view these models as useful for learning but requiring critical use", with trust more skeptical for CV-MCQs than IVDA-MCQs. Self-report only; no effect size.
"Overall, students view these models as useful for learning but requiring critical use (RQ4)."
Discussion¶
Related Claims¶
- In a two-lecture user study, linear mixed models find no statistically significant effect of GPT-generated output on student MCQ performance, with a tendency toward a group-by-attempt interaction for IVDA — related
- Students report negative experiences with ChatGPT including inaccurate outputs, lack of emotional connection, and risk of overreliance, which constrain competence — possibly the same claim (merge candidate)
- Students' top two concerns about educational LLM use are output quality (fabricated or misleading content, 53 mentions) and the verification burden (34 mentions) — a narrower finding that bears on this claim
- The most frequently cited ethical concerns about GenAI in research were hallucinated facts, fabricated references, and misleading content — related
- Students raised ethical concerns about GenAI-assisted data comic creation, most frequently misinformation, bias, ownership, and reduced trustworthiness — related
- The most reported risks of AI tools in science and chemistry education are ethical issues, including gender and racial bias, hallucinations, copyright infringement, and plagiarism — related
- Students perceived the teacher’s modeling with contrasting cases as a turning point that repositioned them as active gatekeepers of AI-generated content — related
- AI support can improve task performance while weakening users' metacognitive accuracy and leaving their own reasoning unimproved — related
- GenAI feedback limitations require students to exercise evaluative judgement — possibly the same claim (merge candidate)
- Most students reported improved output-evaluation competence after comparing their own text analysis with LLM-generated analyses — related
- Interviewed teachers cite poor AI output quality and risks to students' cognitive engagement as main obstacles, and subject-specific use cases as their top professional-development need — related
- Both ChatGPT feedback and peer feedback prompted doctoral students to actively exercise hard evaluative judgement in statistical analysis, through different challenges — related
- Scholars raise three main concerns about AI chatbots in education: reliability and accuracy, fair assessment, and ethical issues — a broader claim this one bears on
- Students see obtaining explanations for their doubts as ChatGPT's most useful role in learning computer science — related
- Students rarely mentioned the adaptive prompts and those who did perceived them as generic and ignored them — related
- General-purpose LLMs show accuracy and reliability limits, including hallucinations and task-specific accuracy below perfect, making them risky as expert advice sources — related
- Students perceived eight GenAI roles but lacked engagement and a sense of achievement — related
- ChatGPT feedback required doctoral students to validate the procedural accuracy of algorithm-generated outputs, with judgement shaped by prompt quality — related
- Teacher-provided correct answers and pre-prompting reduce erroneous LLM output and hallucinations compared with open chatbot use — related
- Assessing AI feedback within PAIRR helped students develop AI literacy and writerly agency, with very few showing overconfidence in AI — related
- Large language models can produce fluent but factually inaccurate or fabricated outputs (AI hallucination), posing risks for uncritical users in science education — related
- A student-facing chatbot was pulled after it reinforced a student's self-deprecating views about math, illustrating substantial risk of direct AI engagement with students — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
students-view-llms-useful-but-requiring-critical-use |
title |
Students perceive LLM chatbots as useful for learning but requiring critical use, reporting hallucinations, overconfidence, and dependency risks |
description |
Students perceive LLM chatbots as useful for learning but requiring critical use, reporting hallucinations, overconfidence, and dependency risks |
status |
review |
evidence_strength |
weak |
newest_evidence |
2026 |
source_checked |
2026-10-20 |
generated |
by process:wiki-ingest on 2026-10-11 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — markos-stamatakis-2026 |
Frontmatter is the page's machine-readable record — see the Schema & Guide.