SavedAdd your journal

Journals / Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi / 2026 / Cilt: 35

CHALLENGES, RISKS, AND ETHICAL CONCERNS IN THE INTEGRATION OF ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: A SYSTEMATIC LITERATURE REVIEW

YÜKSEKÖĞRETİMDE YAPAY ZEKÂ ENTEGRASYONUNDA KARŞILAŞILAN ZORLUKLAR, RİSKLER VE ETİK KAYGILAR: SİSTEMATİK BİR LİTERATÜR TARAMASI

Zafer Kadırhan(ANKARA ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ)0000-0002-7839-5319
Pages
—
Publication Date
2026-07-21
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings underscore that effective AI integration requires clear institutional policies, ethical frameworks, and equitable access, while future research should empirically investigate AI’s impact on learning, cognitive development, and educational equity.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The integration of artificial intelligence (AI) into higher education (HE) has significantly transformed teaching and learning, offering both opportunities and critical challenges.
📊
Key Findings & Data:This systematic literature review (SLR) analyzes 95 peer-reviewed studies published between 2020 and May 2025, retrieved from Web of Science, Scopus, and EBSCO Education Source, focusing on the risks, challenges, and ethical concerns of AI adoption in HE.
💡
Conclusion & Impact:The findings underscore that effective AI integration requires clear institutional policies, ethical frameworks, and equitable access, while future research should empirically investigate AI’s impact on learning, cognitive development, and educational equity.
🔬Design:Review & Systematic Survey
📍Scope:2020 Verileri
⚙️Variables:Yapay zekâ · Yükseköğretim · Zorluklar

Abstract

The integration of artificial intelligence (AI) into higher education (HE) has significantly transformed teaching and learning, offering both opportunities and critical challenges. This systematic literature review (SLR) analyzes 95 peer-reviewed studies published between 2020 and May 2025, retrieved from Web of Science, Scopus, and EBSCO Education Source, focusing on the risks, challenges, and ethical concerns of AI adoption in HE. Four primary themes emerged: 1) Academic Integrity and Misuse of AI Tools, highlighting risks of unethical reliance on AI and threats to cognitive engagement; 2) Data Privacy, Security, and Ethical Concerns, emphasizing the management of personal data, algorithmic bias, and institutional responsibilities; 3) Over-reliance on AI and Its Impact on Learning Outcomes, addressing the potential erosion of critical thinking, problem-solving, and intrinsic motivation; and 4) Equity and Access to AI Tools, focusing on digital divides and the need for inclusive AI literacy. The findings underscore that effective AI integration requires clear institutional policies, ethical frameworks, and equitable access, while future research should empirically investigate AI’s impact on learning, cognitive development, and educational equity.

Özet

Yapay zekânın (YZ) yükseköğretime (YÖ) entegrasyonu, öğretim ve öğrenme süreçlerini önemli ölçüde dönüştürmüş; hem yeni fırsatlar sunmuş hem de çeşitli kritik zorluklar ortaya çıkarmıştır. Bu sistematik literatür taraması (SLR), 2020–Mayıs 2025 tarihleri arasında Web of Science, Scopus ve EBSCO Education Source veri tabanlarından elde edilen 95 hakemli çalışmayı inceleyerek, YZ’nin yükseköğretimde benimsenmesine ilişkin riskler, zorluklar ve etik kaygıları analiz etmektedir. Araştırma sonucunda dört ana tema öne çıkmıştır: 1) Akademik Dürüstlük ve YZ Araçlarının Yanlış Kullanımı, etik dışı YZ kullanım risklerini ve bilişsel katılım üzerindeki tehditleri vurgulamaktadır; 2) Veri Gizliliği, Güvenlik ve Etik Kaygılar, kişisel verilerin yönetimi, algoritmik önyargılar ve kurumsal sorumlulukları ön plana çıkarmaktadır; 3) YZ’ye Aşırı Bağımlılık ve Öğrenme Sonuçları Üzerindeki Etkisi, eleştirel düşünme, problem çözme ve içsel motivasyonun olası azalmasını ele almaktadır; ve 4) YZ Araçlarına Erişim ve Eşitlik, dijital uçurumlara ve kapsayıcı YZ okuryazarlığının gerekliliğine odaklanmaktadır. Bulgular, YZ’nin etkili entegrasyonu için açık kurumsal politikalar, etik çerçeveler ve eşit erişimin gerekliliğini vurgulamakta; ayrıca gelecekteki araştırmaların YZ’nin öğrenme, bilişsel gelişim ve eğitimde adalet üzerindeki etkilerini ampirik olarak incelemesi gerektiğini önermektedir.

🔖

Cite This Article

Kadırhan, Z. (2026). CHALLENGES, RISKS, AND ETHICAL CONCERNS IN THE INTEGRATION OF ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: A SYSTEMATIC LITERATURE REVIEW. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt: 35. https://doi.org/10.35379/cusosbil.1696829

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (72)

  1. Abdelaal, N., & Sawy, I. A. (2024). Perceptions, challenges, and prospects: University professors’ use of artificial intelligence in education. Australian Journal of Applied Linguistics, 7(1), 1309. https://doi.org/10.29140/ajal.v7n1.1309
  2. Adnan, M., Habib, A., Ashraf, J., Mussadiq, S., Raza, A. A., Abid, M., Bashir, M., & Khan, S. U. (2021). Predicting At-Risk students at different percentages of course length for early intervention using machine learning models. IEEE Access, 9, 7519–7539. https://doi.org/10.1109/access.2021.3049446
  3. Agarwal, S., Agarwal, B., & Gupta, R. (2022). Chatbots and virtual assistants: a bibliometric analysis. Library Hi Tech, 40(4), 1013–1030. https://doi.org/10.1108/lht-09-2021-0330
  4. Ahmed, Z., Shanto, S. S., Rime, M. H. K., Morol, M. K., Fahad, N., Hossen, M. J., & Abdullah-Al-Jubair, M. (2024). The Generative AI landscape in Education: mapping the terrain of opportunities, challenges and student perception. IEEE Access, 1. https://doi.org/10.1109/access.2024.3461874
  5. Aldraiweesh, A. A., & Alturki, U. (2025). The Influence of Social Support Theory on AI Acceptance: Examining Educational Support and Perceived Usefulness using SEM analysis. IEEE Access, 1. https://doi.org/10.1109/access.2025.3534099
  6. Ali, O., Murray, P. A., Momin, M., Dwivedi, Y. K., & Malik, T. (2023). The effects of artificial intelligence applications in educational settings: Challenges and strategies. Technological Forecasting and Social Change, 199, 123076. https://doi.org/10.1016/j.techfore.2023.123076
  7. Almasri, F. (2024). Exploring the Impact of Artificial intelligence in teaching and learning of Science: A Systematic Review of Empirical research. Research in Science Education, 54(5), 977–997. https://doi.org/10.1007/s11165-024-10176-3
  8. Al-Mughairi, H., & Bhaskar, P. (2024). Exploring the factors affecting the adoption AI techniques in higher education: insights from teachers’ perspectives on ChatGPT. Journal of Research in Innovative Teaching & Learning. https://doi.org/10.1108/jrit-09-2023-0129
  9. Alotaibi, N. S. (2024). The impact of AI and LMS integration on the future of higher Education: Opportunities, challenges, and strategies for transformation. Sustainability, 16(23), 10357. https://doi.org/10.3390/su162310357
  10. Alotaibi, N. S., & Alshehri, A. H. (2023). Prospers and Obstacles in Using Artificial Intelligence in Saudi Arabia Higher Education Institutions—The Potential of AI-Based Learning Outcomes. Sustainability, 15(13), 10723. https://doi.org/10.3390/su151310723
  11. Alruwais, N. M. (2023). Deep FM-Based predictive model for student dropout in online classes. IEEE Access, 11, 96954–96970. https://doi.org/10.1109/access.2023.3312150
  12. Birkstedt, T., Minkkinen, M., Tandon, A., & Mäntymäki, M. (2023). AI governance: themes, knowledge gaps and future agendas. Internet Research, 33(7), 133–167. https://doi.org/10.1108/intr-01-2022-0042
  13. Cai, L., Msafiri, M. M., & Kangwa, D. (2024). Exploring the impact of integrating AI tools in higher education using the Zone of Proximal Development. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13112-0
  14. Calderon, K., Serrano, N., Blanco, C., & Gutierrez, I. (2023). Automated and continuous assessment implementation in a programming course. Computer Applications in Engineering Education, 32(1). https://doi.org/10.1002/cae.22681
  15. Celik, I. (2022). Towards Intelligent-TPACK: An empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468. https://doi.org/10.1016/j.chb.2022.107468
  16. Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1). https://doi.org/10.1186/s41239-023-00408-3
  17. Chanthiran, M., Ibrahim, A. B., Rahman, M. H. A., & Mariappan, P. (2022). Artificial intelligence in education: A systematic mapping study using Scopus and Web of Science. Journal of ICT in Education, 9(2), 61–70. https://doi.org/10.37134/jictie.vol9.2.5.2022
  18. Chen, L., Chen, P., & Lin, Z. (2020). Artificial Intelligence in Education: a review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/access.2020.2988510
  19. Cicchetti, C. C. (2024). AI in higher education: Does not help, might hurt. Journal of Education for Business, 1–5. https://doi.org/10.1080/08832323.2024.2432936
  20. Colliot, T., Krichen, O., Girard, N., Anquetil, É., & Jamet, É. (2024). What makes tablet‐based learning effective? A study of the role of real‐time adaptive feedback. British Journal of Educational Technology, 55(5), 2278–2295. https://doi.org/10.1111/bjet.13439
  21. Doğan, M., Celik, A., & Arslan, H. (2024). AI in Higher Education: Risks and opportunities from the academician perspective. European Journal of Education. https://doi.org/10.1111/ejed.12863
  22. D’Souza, R. F., Mathew, M., Mishra, V., & Surapaneni, K. M. (2024). Twelve tips for addressing ethical concerns in the implementation of artificial intelligence in medical education. Medical Education Online, 29(1). https://doi.org/10.1080/10872981.2024.2330250
  23. Durak, G., Çankaya, S., Özdemir, D., & Can, S. (2024). Artificial Intelligence in Education: A Bibliometric study on its role in transforming teaching and learning. The International Review of Research in Open and Distributed Learning, 25(3), 219–244. https://doi.org/10.19173/irrodl.v25i3.7757
  24. Erümit, A. K., & Çetin, İ. (2020). Design framework of adaptive intelligent tutoring systems. Education and Information Technologies, 25(5), 4477–4500. https://doi.org/10.1007/s10639-020-10182-8
  25. Espartinez, A. S. (2024). Exploring student and teacher perceptions of ChatGPT use in higher education: A Q-Methodology study. Computers and Education Artificial Intelligence, 7, 100264. https://doi.org/10.1016/j.caeai.2024.100264
  26. Falagas, M. E., Pitsouni, E. I., Malietzis, G. A., & Pappas, G. (2007). Comparison of PubMed, Scopus, Web of Science, and Google Scholar: strengths and weaknesses. The FASEB Journal, 22(2), 338–342. https://doi.org/10.1096/fj.07-9492lsf
  27. Fošner, A. (2024). University Students’ Attitudes and Perceptions towards AI Tools: Implications for Sustainable Educational Practices. Sustainability, 16(19), 8668. https://doi.org/10.3390/su16198668
  28. Gerlich, M. (2025). AI Tools in Society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6. https://doi.org/10.3390/soc15010006
  29. Ghalia, N., Isami, E., Bsoul, M., Aabed, S., Haeb, R. S., & Mustafa, M. (2024). Impact of Artificial Intelligence in Education: Insights from Students and Faculty Members at Yarmouk University. Journal of Ecohumanism, 3(8). https://doi.org/10.62754/joe.v3i8.4807
  30. Ghiasvand, F., Kogani, M., & Alipoor, A. (2024). “I’m Not Ready for This Metamorphosis”: An Ecological Approach to Iranian and Italian EFL Teachers’ Readiness for AI-Mediated Instruction. Teaching English With Technology. https://doi.org/10.56297/vaca6841//bffo7057/iski2001
  31. Groothuijsen, S., Van Den Beemt, A., Remmers, J. C., & Van Meeuwen, L. W. (2024). AI chatbots in programming education: students’ use in a scientific computing course and consequences for learning. Computers and Education Artificial Intelligence, 7, 100290. https://doi.org/10.1016/j.caeai.2024.100290
  32. Grossmann, I., Feinberg, M., Parker, D. C., Christakis, N. A., Tetlock, P. E., & Cunningham, W. A. (2023). AI and the transformation of social science research. Science, 380(6650), 1108–1109. https://doi.org/10.1126/science.adi1778
  33. Gulson, K. N., & Witzenberger, K. (2020). Repackaging authority: artificial intelligence, automated governance and education trade shows. Journal of Education Policy, 37(1), 145–160. https://doi.org/10.1080/02680939.2020.1785552
  34. Gusenbauer, M., & Haddaway, N. R. (2019). Which academic search systems are suitable for systematic reviews or meta‐analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Research Synthesis Methods, 11(2), 181–217. https://doi.org/10.1002/jrsm.1378
  35. Han, B., Nawaz, S., Buchanan, G., & McKay, D. (2025). Students’ Perceptions: Exploring the interplay of ethical and pedagogical impacts for adopting AI in higher education. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00456-4
  36. Ifenthaler, D., Majumdar, R., Gorissen, P., Judge, M., Mishra, S., Raffaghelli, J., & Shimada, A. (2024). Artificial intelligence in Education: Implications for policymakers, researchers, and practitioners. Technology Knowledge and Learning. https://doi.org/10.1007/s10758-024-09747-0
  37. Ivanov, S. (2023). The dark side of artificial intelligence in higher education. Service Industries Journal, 43(15–16), 1055–1082. https://doi.org/10.1080/02642069.2023.2258799
  38. Jain, K. K., & Raghuram, J. N. V. (2024). Gen-AI integration in higher education: Predicting intentions using SEM-ANN approach. Education and Information Technologies, 29(13), 17169–17209. https://doi.org/10.1007/s10639-024-12506-4
  39. Karkoulian, S., Sayegh, N., & Sayegh, N. (2024). ChatGPT unveiled: Understanding Perceptions of Academic Integrity in Higher Education - A Qualitative Approach. Journal of Academic Ethics. https://doi.org/10.1007/s10805-024-09543-6
  40. Kavadella, A., Da Silva, M. a. D., Kaklamanos, E. G., Stamatopoulos, V., & Giannakopoulos, K. (2023). Evaluation of CHATGPT’s Real-Life Implementation in Undergraduate Dental Education: Mixed Methods study. JMIR Medical Education, 10, e51344. https://doi.org/10.2196/51344
  41. Khalifa, M., & Albadawy, M. (2024). Using artificial intelligence in academic writing and research: An essential productivity tool. Computer Methods and Programs in Biomedicine Update, 5, 100145. https://doi.org/10.1016/j.cmpbup.2024.100145
  42. Klimova, B., Pikhart, M., & Kacetl, J. (2023). Ethical issues of the use of AI-driven mobile apps for education. Frontiers in Public Health, 10. https://doi.org/10.3389/fpubh.2022.1118116
  43. Knopp, M. I., Warm, E. J., Weber, D., Kelleher, M., Kinnear, B., Schumacher, D. J., Santen, S. A., Mendonça, E., & Turner, L. (2023). AI-Enabled medical Education: threads of change, promising futures, and risky realities across four potential future worlds. JMIR Medical Education, 9, e50373. https://doi.org/10.2196/50373
  44. Knox, J. (2020). Artificial intelligence and education in China. Learning Media and Technology, 45(3), 298–311. https://doi.org/10.1080/17439884.2020.1754236
  45. Lai, T., Zeng, X., Xu, B., Xie, C., Liu, Y., Wang, Z., Lu, H., & Fu, S. (2023). The application of artificial intelligence technology in education influences Chinese adolescent’s emotional perception. Current Psychology, 43(6), 5309–5317. https://doi.org/10.1007/s12144-023-04727-6
  46. Li, M. (2024). Integrating Artificial intelligence in primary Mathematics Education: Investigating internal and external influences on teacher adoption. International Journal of Science and Mathematics Education. https://doi.org/10.1007/s10763-024-10515-w
  47. Li, Z., & Zhang, W. (2024). Technology in education: Addressing legal and governance challenges in the digital era. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13036-9
  48. Lünich, M., Keller, B., & Marcinkowski, F. (2024). Diverging perceptions of artificial intelligence in Higher Education: A comparison of student and public assessments on risks and damages of academic performance prediction in Germany. Computers and Education Artificial Intelligence, 100305. https://doi.org/10.1016/j.caeai.2024.100305
  49. Ma, W., Ma, W., Hu, Y., & Bi, X. (2024). The who, why, and how of ai-based chatbots for learning and teaching in higher education: A systematic review. Education and Information Technologies. https://doi.org/10.1007/s10639-024-13128-6
  50. Mekterovic, I., Brkic, L., Milasinovic, B., & Baranovic, M. (2020). Building a Comprehensive Automated Programming Assessment System. IEEE Access, 8, 81154–81172. https://doi.org/10.1109/access.2020.2990980
  51. Messer, M., Brown, N. C. C., Kölling, M., & Shi, M. (2023). Automated Grading and Feedback Tools for Programming Education: A Systematic review. ACM Transactions on Computing Education, 24(1), 1–43. https://doi.org/10.1145/3636515
  52. Monib, W. K., Qazi, A., Apong, R. A., Azizan, M. T., De Silva, L., & Yassin, H. (2024). Generative AI and future education: a review, theoretical validation, and authors’ perspective on challenges and solutions. PeerJ Computer Science, 10, e2105. https://doi.org/10.7717/peerj-cs.2105
  53. Msambwa, M. M., Wen, Z., & Daniel, K. (2025). The impact of AI on the personal and collaborative learning environments in higher education. European Journal of Education, 60(1). https://doi.org/10.1111/ejed.12909
  54. Ng, D. T. K., Lee, M., Tan, R. J. Y., Hu, X., Downie, J. S., & Chu, S. K. W. (2022). A review of AI teaching and learning from 2000 to 2020. Education and Information Technologies, 28(7), 8445–8501. https://doi.org/10.1007/s10639-022-11491-w
  55. Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. T. (2022). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
  56. Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71
  57. Panit, N. M. (2025). Can critical thinking and AI work together? Observations of science, Mathematics, and language instructors. Environment and Social Psychology, 9(11). https://doi.org/10.59429/esp.v9i11.3141
  58. Plecerda, L. P. (2024). Academic integrity surrounding the use of generative AI in higher education: Lenses from ICT college students. Environment and Social Psychology, 9(12), 3177. https://doi.org/10.59429/esp.v9i12.3177
  59. Rahim, N. I. M., Iahad, N. A., Yusof, A. F., & Al-Sharafi, M. A. (2022). AI-Based Chatbots Adoption Model for Higher-Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach. Sustainability, 14(19), 12726. https://doi.org/10.3390/su141912726
  60. Saif, N., Khan, S. U., Shaheen, I., ALotaibi, F. A., Alnfiai, M. M., & Arif, M. (2023). Chat-GPT; validating Technology Acceptance Model (TAM) in education sector via ubiquitous learning mechanism. Computers in Human Behavior, 154, 108097. https://doi.org/10.1016/j.chb.2023.108097
  61. Saihi, A., Ben-Daya, M., Hariga, M., & As’ad, R. (2024). A Structural equation modeling analysis of generative AI chatbots adoption among students and educators in higher education. Computers and Education Artificial Intelligence, 7, 100274. https://doi.org/10.1016/j.caeai.2024.100274
  62. Schiff, D. (2021). Education for AI, not AI for Education: The Role of Education and Ethics in National AI Policy Strategies. International Journal of Artificial Intelligence in Education, 32(3), 527–563. https://doi.org/10.1007/s40593-021-00270-2
  63. Steyvers, M., & Kumar, A. (2023). Three challenges for AI-Assisted Decision-Making. Perspectives on Psychological Science, 19(5), 722–734. https://doi.org/10.1177/17456916231181102
  64. Stracke, C. M., Griffiths, D., Pappa, D., Bećirović, S., Polz, E., Perla, L., Di Grassi, A., Massaro, S., Skenduli, M. P., Burgos, D., Punzo, V., Amram, D., Ziouvelou, X., Katsamori, D., Gabriel, S., Nahar, N., Schleiss, J., & Hollins, P. (2025). Analysis of artificial intelligence policies for higher education in Europe. International Journal of Interactive Multimedia and Artificial Intelligence, In Press(In Press), 1–14. https://doi.org/10.9781/ijimai.2025.02.011
  65. Swindell, A., Greeley, L., Farag, A., & Verdone, B. (2024). Against Artificial Education: Towards an ethical framework for generative Artificial intelligence (AI) use in education. Online Learning, 28(2). https://doi.org/10.24059/olj.v28i2.4438
  66. Wohlin, C. (2014). Guidelines for snowballing in systematic literature studies and a replication in software engineering. ACM, 1–10. https://doi.org/10.1145/2601248.2601268
  67. Xu, S., Chen, P., & Zhang, G. (2024). Exploring Chinese university educators’ acceptance and intention to use AI tools: an application of the UTAUT2 model. SAGE Open, 14(4). https://doi.org/10.1177/21582440241290013
  68. Zaim, M., Arsyad, S., Waluyo, B., Ardi, H., Hafizh, M. A., Zakiyah, M., Syafitri, W., Nusi, A., & Hardiah, M. (2024). AI-Powered EFL Pedagogy: Integrating Generative AI into University Teaching Preparation through UTAUT and Activity Theory. Computers and Education Artificial Intelligence, 7, 100335. https://doi.org/10.1016/j.caeai.2024.100335
  69. Zhang, J., & Zhang, Z. (2024). AI in teacher education: Unlocking new dimensions in teaching support, inclusive learning, and digital literacy. Journal of Computer Assisted Learning, 40(4), 1871–1885. https://doi.org/10.1111/jcal.12988
  70. Zhang, L., & Xu, J. (2024). The paradox of self-efficacy and technological dependence: Unraveling generative AI’s impact on university students’ task completion. The Internet and Higher Education, 65, 100978. https://doi.org/10.1016/j.iheduc.2024.100978
  71. Zhang, W., Cai, M., Lee, H. J., Evans, R., Zhu, C., & Ming, C. (2023). AI in Medical Education: Global situation, effects and challenges. Education and Information Technologies. https://doi.org/10.1007/s10639-023-12009-8
  72. Zheng, L., Niu, J., Zhong, L., & Gyasi, J. F. (2021). The effectiveness of artificial intelligence on learning achievement and learning perception: A meta-analysis. Interactive Learning Environments, 31(9), 5650–5664. https://doi.org/10.1080/10494820.2021.2015693