Actionable companion · Assessment & reasoning
Learning with agency: Beyond boundaries: Navigating the positive potential of ChatGPT, empowering education in underdeveloped corners of the world
AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.
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 participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
Abstract In November 2022, ChatGPT3, an advanced AI-powered chatbot, emerged suddenly, generating significant interest in higher education. Concerns arose regarding its potential to complicate the authentication of genuine student work. While some foresaw negative impacts, we advocate a positive outlook, suggesting educators can utilize ChatGPT to cultivate supportive learning environments that enhance students' character development. Our study thoroughly explores ChatGPT's implications for education in underdeveloped countries, examining both opportunities and challenges within AI research in education. We investigate practical applications, and potential benefits, and propose responsible integration strategies for students, teachers, and schools in utilizing ChatGPT for learning and assessment. Emphasizing ethical use, we stress leadership, character development, and authentic assessment as crucial factors. Despite concerns about academic integrity, we highlight ChatGPT's dual nature, it can facilitate cheating but also has the potential to deepen learning experiences. Our research focuses on understanding ChatGPT's impact on education from both student and teacher perspectives, discussing future trends in learning and teaching. The strategic integration of ChatGPT and AI in universities demands ethical foresight, personalized learning strategies, and ongoing research to optimize educational benefits while preserving core values and fostering student development. Keywords: ChatGPT, Artificial intelligence, Higher education, Authentic student work, Ethical writing
What kind of evidence is this?
Conversational & pedagogical agents · Design requires full-text appraisal
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.
Limitations and uncertainty · source passage
The potential hindrance to long-term cognitive development and creativity, as highlighted by Rudolph et al. [5], takes on unique dimensions in underdeveloped countries. Here, limitations in educational infrastructure and resources may compound the challenges faced by students who overly depend on ChatGPT, impeding their ability to independently accumulate knowledge. Moreover, the shift away from seeking assistance from traditional sources, as noted by Arif et al. [9], may be amplified in regions where educational support systems are under-resourced. The diminished development of collaboration and communication skills, critical for success in various professional fields, becomes a pressing concern in the context of underdeveloped countries.
Design the system before the pilot
Define whether the agent asks questions, gives hints, explains or assesses. Test hallucinated feedback, excessive praise, misleading confidence and responses when the learner is confused. A conversational interface alone does not establish autonomous agency.
- Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.
A 90-day district pilot
- 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.
- 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.
- 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.
- 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
Amna Shabbir; Safdar Rizvi; Muhammad Mansoor Alam; Mazliham Mohd Su'ud. Beyond boundaries: Navigating the positive potential of ChatGPT, empowering education in underdeveloped corners of the world. Heliyon (2024-08-08). https://doi.org/10.1016/j.heliyon.2024.e35845
Companion prepared 2026-10-30. 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.