Transforming education with AI: A systematic review of ChatGPT's role in learning, academic practices, and institutional adoption
Open accessAbstract
• This research provides a comparative examination of AI-supported and traditional teaching methods, assisting educators and policymakers in making informed decisions about incorporating AI into their curricula. • By assessing how ChatGPT performs in academic writing, literature reviews, and translation, this study sheds light on the strengths and limitations of ChatGPT in these key academic areas. The findings will help researchers and students use ChatGPT successfully, as well as highlight areas that require human oversight. • This research offers insights into how global higher educational institutions navigate the challenges and opportunities presented by AI tools like ChatGPT, helping to define best practices and guidelines for the ethical and effective use of AI in academic contexts and thereby upholding academic standards and integrity. • This study enhances the body of knowledge on AI's role in education by investigating how tools like ChatGPT can support educators in professional development, with specific emphasis on teaching preparation and assessment. It offers insights into how such tools can be effectively utilized to improve classroom outcomes and instructional strategies. The integration of AI tools like ChatGPT into education has generated significant interest due to their potential to transform learning environments by providing personalized learning, automating tasks, and improving student engagement. However, gaps remain in the literature, particularly in comparing AI-supported education methods with traditional approaches and understanding ChatGPT's specific role in academic writing, literature reviews, and teacher development. This study addresses these gaps through a systematic literature review (SLR), evaluating the effectiveness of AI tools versus traditional teaching approaches. It focuses on ChatGPT's application in areas such as academic writing, lesson planning, student assessment, and the professional development of educators. Additionally, the study explores institutional strategies for balancing the benefits of AI with the need to maintain academic integrity and educational quality. The methodology involved a systematic search across academic databases with a structured analysis of key studies in AI-supported education. The main contributions include identifying the comparative advantages and limitations of AI in enhancing student learning, offering best practices for incorporating AI tools into teaching, and examining prompt engineering as a crucial factor in optimizing AI usage. The study reveals that ChatGPT and other AI tools significantly enhance educational efficiency and engagement, but they require careful management to prevent over-reliance. The study advocates for a balanced approach, integrating both AI and traditional methods to achieve optimal educational outcomes while maintaining academic integrity.
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These policies establish clear governance structures that define acceptable AI use, ensuring that AI tools support learning rather than facilitate misconduct.
“These policies establish clear governance structures that define acceptable AI use, ensuring that AI tools support learning rather than facilitate misconduct [ 54, 57].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [57]
The integration of AI tools like ChatGPT and other generative models makes it difficult for educators to differentiate between authentic student work and AI-generated content.
“The integration of AI tools like ChatGPT and other genera -tive models has significantly impacted academic integrity, making it difficult for educators to differentiate between authentic student work and AI-generated content [ 53, 54, 57, 76-78 ].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [57]
Improving AI detection tools to accurately identify AI-generated content while reducing false positives and negatives is an approach to maintain academic integrity.
“One approach involves improving AI detec -tion tools to accurately identify AI-generated content while reducing false positives and negatives that may affect aca -demic integrity enforcement [ 31, 54, 57, 66, 91, 106].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [57]
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Security and Privacy Concerns in AI Models
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Flagged first, then by citations
Cited by
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“These policies establish clear governance structures that define acceptable AI use, ensuring that AI tools support learning rather than facilitate misconduct [ 54, 57].”
InfluentialLee, H., Huang, Y., & Wu, T. (2025) · European Journal of Education · doi:10.1111/ejed.70262 · Cited by 31 · see citing works
Zhu, Y., Jiang, H., & Zhong, L. (2026) · European Journal of Education · doi:10.1111/ejed.70462 · Cited by 23 · see citing works
The perceived impact of artificial intelligence on academic learning
Dogaru, M., Pisică, O., Popa, C.-Ș., Răgman, A.-A., & Tololoi, I.-R. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1611183 · Cited by 34 · see citing works
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ChatGPT Told Me to Say It: AI Chatbots and Class Participation Apprehension in University Students
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AI-Enhanced Cyber Science Education: Innovations and Impacts
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AI Gem: Context-Aware Transformer Agents as Digital Twin Tutors for Adaptive Learning
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Hu, Y.-H., Yu, H.-Y., & Hsieh, C.-L. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13784-2 · Cited by 9 · see citing works
Jaiswal, R., Prakhar, P., Chen, P.-J., & Gupta, S. (2026) · Journal of Hospitality & Tourism Education · doi:10.1080/10963758.2025.2602583 · Cited by 5 · see citing works
Artificial intelligence reveals unbalanced sustainability domains in funded research
Varelas, P., Larosa, F., Hoyas, S., Conejero, J. A., Contino, F., Nerini, F. F., García‐Martínez, J., Garibo‐i‐Orts, Ò., Parente, A., & Vinuesa, R. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.104367 · Cited by 5 · see citing works
Harnessing AI for Inclusive Teaching: Professional Development Strategies for Early Educators
Marras, A., Fornara, F., & Pasqualotto, A. (2025) · Wiley eBooks · doi:10.1002/9781394352821.ch16 · Cited by 5 · see citing works
Wang, R., Liu, C., & Yi, F. (2025) · Medical Education · doi:10.1111/medu.70100 · Cited by 6 · see citing works
AI Competence of Teachers in a Developing Nation
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ChatGPT ‐Generated Versus Teacher‐Prepared Read‐Aloud Plans: A Comparative Effectiveness Study
SÖNMEZ, M., Öksüz, H. İ., Gök, S., & Yıldırım, K. (2026) · European Journal of Education · doi:10.1111/ejed.70458 · Cited by 3 · see citing works
Artificial Intelligence in Gifted STEAM Education
Milakis, E. D., Argyrakou, C. C., & Samala, A. D. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-2600-0101-1.ch008 · Cited by 2 · see citing works
From rules to language models: a comparative study of chatbot learning assistants
Smutný, P., & Schreiberova, P. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1794807 · Cited by 3 · see citing works
Student AI resistance: status anxiety and trust in Kazakhstani higher education
Cespedes, A. A., & Courtney, M. G. R. (2026) · Asian Education and Development Studies · doi:10.1108/aeds-11-2025-0604 · Cited by 2 · see citing works
Initial Validation of the IMPACT Model: Technological Appropriation of ChatGPT by University Faculty
Pereira, L. M., Basantes-Andrade, A., Naranjo-Toro, M., & Guia-Pereira, M. (2025) · Education Sciences · doi:10.3390/educsci15111520 · Cited by 3 · see citing works
Exploring the Merchera Ethnic Group Through ChatGPT: The Risks of Epistemic Exclusion
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Role of Teachers in the Age of Artificial Intelligence: An Analytic-philosophical Perspective
Park, J.-B. (2025) · Journal of Lifelong Learning Society · doi:10.26857/jlls.2025.11.21.4.1 · Cited by 1 · see citing works
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Transformation of Higher Education: The potentials and challenges of GenAI implementation
Dumančić, M., Ružić-Baf, M., & Kadum, S. (2025) · doi:10.5937/int-themed-bg25101d · Cited by 1 · see citing works
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User Experience Analysis: ChatGPT-based AI in Islamic Religious Education Learning
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La Inteligencia Artificial y Su Influencia en El Sistema Educativo Contemporáneo.
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AI Application Impact on Higher Education Institutions in Poland
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Unmasking CAMEO cheating in MOOCs via behavioral and temporal analysis without IP tracking
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Unlocked Learning: How Educators Navigate AI in Open Education Spaces
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The Role of Aritificial Intelligence in Educational Assessment: The Case of Vietnam
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Menyiapkan Generasi Cerdas Digital : Pengenalan Konsep AI di SMPN 3 Gondang Mojokerto
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The Evolution of Generative Artificial Intelligence in Education
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Embracing Digital Transformation
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- Authors
- Salih, S., Husain, O., Hamdan, M., Abdelsalam, S., Elshafie, H., & Motwakel, A.
- Affiliations
- Prince Sattam Bin Abdulaziz University (SA)
- University of Khartoum (SD)
- National College of Ireland (IE)
- King Khalid University (SA)
- Corresponding author
- Husain, Omayma
- Venue
- Results in Engineering
- Publisher
- Elsevier BV
- Published
- 2025-03
- Volume
- 25
- Pages
- 103837
- ISSN
- 2590-1230 (ISSN-L)
- OpenAlex
- W4405915720
- Cited by
- 74
- References
- 110
- Citations by year
- 2026: 40 · 2025: 34
- FWCI
- 4.28
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Results in Engineering (publishedVersion, cc-by-nc-nd)
- TRAP@NCI (National College of Ireland) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- King Khalid University Deanship of Scientific Research (RGP 2/291/45)
- King Khalid University
- Prince Sattam bin Abdulaziz University (PSAU/2024/R/1445)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- ChatGPT, generative AI in education, academic writing, machine translation, teacher professional development, lesson planning, student assessment, academic integrity, prompt engineering, AI adoption in higher education, overreliance on AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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