Agentic education · Learning science

Actionable companion · Motivation & engagement

From AI evidence to responsible practice: Drivers and Consequences of ChatGPT Use in Higher Education: Key Stakeholder Perspectives

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participants, intervention and comparator before applying this study in a new setting.

Original authors’ abstract, reproduced under the linked source license:

Abstract The incorporation of artificial intelligence (AI) into education has heralded a transformative era in the way students learn and faculties teach. Among the burgeoning array of AI tools, ChatGPT stands out as a versatile and powerful resource. Developed by OpenAI, ChatGPT is an AI-driven conversational model that generates human-like responses. This research draws on the Constructivism Learning Theory to uncover the key drivers pushing higher education students to use ChatGPT for academic purposes, and the multifaceted consequences it brings to the academic environment, by integrating the perspectives of key stakeholders: students, faculty, and education experts/leaders. The key findings of in-depth, face-to-face, interviews with key stakeholders revealed 12 main drivers that motivate students and their faculty to use ChatGPT mainly for learning purposes. However, the findings identified the multifaceted (six positive and another six negative) consequences of using ChatGPT for academic purposes. Recommendations for mitigating the negative consequences of ChatGPT were discussed with key stakeholders, particularly education experts/leaders, who were more concerned about using ChatGPT for academic reasons. The research reveals that higher education institutions should establish clear guidelines as a part of higher education policy, supplemented with training sessions for students and their faculty, about the responsible use of ChatGPT for academic purposes to mitigate any ethical concerns. Keywords: ChatGPT, AI, higher education, chatbot, holistic perspective, drivers, consequences

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This research undertook a qualitative methodology in answering the research questions and fulfilling the research objectives. The qualitative methodology was found to be more suitable than quantitative approach for giving a better insight and gaining in-depth information about the drivers and consequences of ChatGPT use for academic purposes in higher education [36]. This research used the consolidated criteria for reporting qualitative studies (QCREQ) [37]. Qualitative research often focuses on the meanings, experiences and perceptions reported in interviews [36,37]; hence, it does not aim to collect quantitative data or report statistical information. All the guidelines suggested in the QCREQ were followed while undertaking the research, processing the data and reporting the findings. With regard to the data collection methods, the research adopted in-depth, face-to-face interviews [38]. We sought to find answers to the research questions about the perceptions of educational stakeholders (i.e., students, faculty, and education leaders) regarding ChatGPT use in higher education, especially its drivers and various consequences. Four core themes were discussed with the three key stakeholders:

Limitations and uncertainty · source passage

On the other hand, there were other prominent key challenges concerning using ChatGPT in education. These challenges include reliability issues, the reliance on biased data [6], the limitation of knowledge to information available up to 2021 [7], relying on inaccurate or fabricated content, including fictitious citations [8], and over-reliance on ChatGPT might have negative impact students’ critical thinking and problem-solving abilities [9]. GhatGPT could represent a threat to academic ethics; the response of higher education institutions (HEIs) varies from those who have raced to enforce full constraints on the usage of ChatGPT [7,8] to those who have begun to support it by posting guidelines on how to deal with AI in an ethical and proper way [9]. Notwithstanding this, the majority of HEIs did not publish any information for their students concerning the use of ChatGPT for academic purposes, regarding whether it is permitted or not for academic purposes [10,11].

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Ahmed M Hasanein; Abu Elnasr E Sobaih. Drivers and Consequences of ChatGPT Use in Higher Education: Key Stakeholder Perspectives. European Journal of Investigation in Health, Psychology and Education (2023-11-09). https://doi.org/10.3390/ejihpe13110181

Companion prepared 2026-11-01. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.