⚠ Official Notice: www.ijisrt.com is the official website of the International Journal of Innovative Science and Research Technology (IJISRT) Journal for research paper submission and publication. Please beware of fake or duplicate websites using the IJISRT name.



The Mediated Impact of Generative AI on Academic Outcomes: A Conceptual Framework Integrating Psychological and Ethical Perspectives in Higher Education


Authors : Niteegya Bhushan

Volume/Issue : Volume 11 - 2026, Issue 4 - April


Google Scholar : https://tinyurl.com/mej47vba

Scribd : https://tinyurl.com/2hyrwfbz

DOI : https://doi.org/10.38124/ijisrt/26apr209

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.


Abstract : The expeditious evolution of generative artificial intelligence (GenAI) is fundamentally reshaping pedagogy, learning settings, and academic practices within higher education. Despite a proliferation of literature on GenAI in education, the field remains fragmented across technological, pedagogical, and psychological domains. This review synthesizes recent literature (2020–2025), focusing on GenAI's role in supporting academic achievement via AI-mediated digital practices. The analysis specifically examines how key psychological mediators including motivation, cognitive engagement, self-regulated learning, and emotional engagement interact with GenAI use. Furthermore, the paper addresses critical ethical and governance challenges related to GenAI adoption, such as algorithmic bias, academic integrity, data governance, and student privacy. Based on this synthesis, a conceptual framework is proposed to explain how GenAI can effectively promote academic achievement when integrated within ethically responsible and pedagogically sound educational environments.

Keywords : Generative Artificial Intelligence; Academic Achievement; Higher Education; Educational Technology; Student Engagement.

References :

  1. Baig, M. I., & Yadegaridehkordi, E. (2024). ChatGPT in higher education: A systematic literature review and research challenges. International Journal of Educational Research, 108, 102411. https://doi.org/10.1016/j.ijer.2024.102411
  2. Bin-Nashwan, S. A., Sadallah, M., & Bouteraa, M. (2023). Use of ChatGPT in academia: Academic integrity hangs in the balance. Technology in Society, 75, 102370.
    https://doi.org/10.1016/j.techsoc.2023.102370
  3. Bond, M., Khosravi, H., De Laat, M., Bergdahl, N., Negrea, V., Oxley, E., Pham, P., & Chong, S. W. (2024). A meta systematic review of artificial intelligence in higher education: A call for increased ethics, collaboration, and rigour. International Journal of Educational Technology in Higher Education, 21, 4. https://doi.org/10.1186/s41239-023-00436-z
  4. Boubker, O. (2024). From chatting to self-educating: Can AI tools boost student learning outcomes? Expert Systems with Applications, 238, 121820.
    https://doi.org/10.1016/j.eswa.2023.121820
  5. Bozkurt, A., & Sharma, R. C. (2023). Challenging the status quo and exploring the new boundaries in the age of algorithms: Reimagining the role of generative AI in distance education and online learning. Asian Journal of Distance Education, 18(1).
    https://doi.org/10.5281/zenodo.7755273
  6. Bozkurt, A., Xiao, J., Lambert, S., Pazurek, A., Crompton, H., Koseoglu, S., et al. (2023).
    Speculative futures on ChatGPT and generative artificial intelligence in education: A collective reflection from the educational landscape. Asian Journal of Distance Education, 18(1), 1–78.
  7. Cassidy, S. (2015). Resilience building in students: The role of academic self-efficacy. Frontiers in Psychology, 6, 1781.
    https://doi.org/10.3389/fpsyg.2015.01781
  8. 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, 38. https://doi.org/10.1186/s41239-023-00411-8
  9. Chan, C. K. Y., & Hu, W. (2023). Students’ voices on generative AI: Perceptions, benefits, and challenges in higher education. International Journal of Educational Technology in Higher Education, 20, 43. https://doi.org/10.1186/s41239-023-00408-3
  10. Charow, R., Jeyakumar, T., Younus, S., et al. (2021). Artificial intelligence education programs for healthcare professionals: A scoping review. JMIR Medical Education, 7(4), e31043.
    https://doi.org/10.2196/31043
  11. Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education: Contributors, collaborations, research topics, challenges, and future directions. Educational Technology & Society, 25(1), 28–47.
    https://doi.org/10.2307/48647028
  12. Cobos, C., Rodriguez, O., Rivera, J., Betancourt, J., Mendoza, M., León, E., & Herrera-Viedma, E. (2013). A hybrid system of pedagogical pattern recommendations based on singular value decomposition and variable data attributes. Information Processing & Management, 49(3), 607–625.
    https://doi.org/10.1016/j.ipm.2012.12.002
  13. Cotton, D. R., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International.
    https://doi.org/10.1080/14703297.2023.2190148
  14. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20, 22.
    https://doi.org/10.1186/s41239-023-00392-8
  15. David, L., Biwer, F., Baars, M., Wijnia, L., Paas, F., & de Bruin, A. (2024).
    The relation between perceived mental effort, monitoring judgments, and learning outcomes: A meta-analysis. Educational Psychology Review, 36, 66.

    https://doi.org/10.1007/s10648-024-09903-z
  16. Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2024). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. https://doi.org/10.1016/j.compedu.2024.105224
  17. Doménech-Betoret, F., Abellán-Roselló, L., & Gómez-Artiga, A. (2017). Self-efficacy, satisfaction, and academic achievement: The mediator role of students’ expectancy-value beliefs. Frontiers in Psychology, 8, 1193.
    https://doi.org/10.3389/fpsyg.2017.01193
  18. Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., … Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
    https://doi.org/10.1016/j.ijinfomgt.2023.102642
  19. Fauzi, F., Tuhuteru, L., Sampe, F., Ausat, A. M. A., & Hatta, H. R. (2023).
    Analysing the role of ChatGPT in improving student productivity in higher education. Journal on Education, 5(4), 14886–14891.

    https://doi.org/10.31004/joe.v5i4.2648
  20. Grassini, S. (2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13, 692.
    https://doi.org/10.3390/educsci13070692
  21. Greene, B. A. (2015). Measuring cognitive engagement with self-report scales: Reflections from over 20 years of research. Educational Psychologist, 50(1), 14–30.
    https://doi.org/10.1080/00461520.2014.989230
  22. Greene, B. A., & Miller, R. B. (1996). Influences on achievement: Goals, perceived ability, and cognitive engagement. Contemporary Educational Psychology, 21(2), 181–192.
    https://doi.org/10.1006/ceps.1996.0015
  23. Habibi, A., Muhaimin, M., Danibao, B. K., Wibowo, Y. G., Wahyuni, S., & Octavia, A. (2023).
    ChatGPT in higher education learning: Acceptance and use. Computers and Education: Artificial Intelligence, 5, 100190.

    https://doi.org/10.1016/j.caeai.2023.100190
  24. Haugeland, J. (1985). Artificial intelligence: The very idea. MIT Press.
  25. Hoffait, A.-S., & Schyns, M. (2017). Early detection of university students with potential difficulties. Decision Support Systems, 101, 1–11.
    https://doi.org/10.1016/j.dss.2017.05.003
  26. Honicke, T., & Broadbent, J. (2016). The influence of academic self-efficacy on academic performance: A systematic review. Educational Research Review, 17, 63–84.
    https://doi.org/10.1016/j.edurev.2015.11.002
  27. Howard, C., Jordan, P., di Eugenio, B., & Katz, S. (2017). Shifting the load: A peer dialogue agent that encourages its human collaborator to contribute more to problem solving. International Journal of Artificial Intelligence in Education, 27(1), 101–129.
    https://doi.org/10.1007/s40593-015-0071-y
  28. Howard, E., Meehan, M., & Parnell, A. (2018). Contrasting prediction methods for early warning systems at undergraduate level. Internet and Higher Education, 37, 66–75.
    https://doi.org/10.1016/j.iheduc.2018.02.001
  29. Huallpa, J. J. (2023). Exploring the ethical considerations of using ChatGPT in university education. Periodicals of Engineering and Natural Sciences, 11(4), 105–115.
    https://doi.org/10.21533/pen.v11i4.3913
  30. Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., … Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
    https://doi.org/10.1016/j.lindif.2023.102274
  31. Li, X. (2007). Intelligent agent-supported online education. Decision Sciences Journal of Innovative Education, 5(2), 311–331.
    https://doi.org/10.1111/j.1540-4609.2007.00143.x
  32. Liang, J., Wang, L., Luo, J., & Yan, Y. (2023). The relationship between student interaction with generative artificial intelligence and learning achievement: Serial mediating roles of self-efficacy and cognitive engagement. Frontiers in Psychology, 14, 1285392. https://doi.org/10.3389/fpsyg.2023.1285392
  33. Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023).
    Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790.
    https://doi.org/10.1016/j.ijme.2023.100790
  34. Maheshwari, G. (2023).
    Factors influencing students’ intention to adopt and use ChatGPT in higher education. Education and Information Technologies.

    https://doi.org/10.1007/s10639-023-11852-4
  35. Monzon, N., & Hays, F. A. (2025). Leveraging generative artificial intelligence to improve motivation and retrieval in higher education learners. JMIR Medical Education, 11(1), e59210. https://doi.org/10.2196/59210
  36. Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023).
    On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing, 40(2), 269–275.

    https://doi.org/10.1016/j.ijresmar.2023.03.001
  37. Pietarinen, J., Soini, T., & Pyhältö, K. (2014). Students’ emotional and cognitive engagement as the determinants of well-being and achievement in school. International Journal of Educational Research, 67, 40–51. https://doi.org/10.1016/j.ijer.2014.05.001
  38. Robbins, S. B., Lauver, K., Le, H., Davis, D., Langley, R., & Carlstrom, A. (2004).
    Do psychosocial and study skill factors predict college outcomes? Psychological Bulletin, 130(2), 261–288.

    https://doi.org/10.1037/0033-2909.130.2.261
  39. Schiaffino, S., Garcia, P., & Amandi, A. (2008).
    eTeacher: Providing personalized assistance to e-learning students. Computers & Education, 51(4), 1744–1754.

    https://doi.org/10.1016/j.compedu.2008.05.008
  40. Sedaghat, M., Abedin, A., Hejazi, E., & Hassanabadi, H. (2011). Motivation, cognitive engagement, and academic achievement. Procedia – Social and Behavioral Sciences, 15, 2406–2410. https://doi.org/10.1016/j.sbspro.2011.04.117
  41. Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
  42. Spikol, D., Ruffaldi, E., Dabisias, G., & Cukurova, M. (2018).
    Supervised machine learning in multimodal learning analytics for estimating success in project-based learning. Journal of Computer Assisted Learning, 34(4), 366–377.

    https://doi.org/10.1111/jcal.12263
  43. Steenbergen-Hu, S., & Cooper, H. (2014).
    A meta-analysis of the effectiveness of intelligent tutoring systems on college students’ academic learning. Journal of Educational Psychology, 106(2), 331–347.

    https://doi.org/10.1037/a0034752
  44. Sun, H. L., Sun, T., Sha, F. Y., Gu, X. Y., & Hou, X. R. (2022).
    The influence of teacher–student interaction on the effects of online learning: A serial mediating model. Frontiers in Psychology, 13, 779217.

    https://doi.org/10.3389/fpsyg.2022.779217
  45. Sun, L., & Zhou, L. (2024). Does generative artificial intelligence improve the academic achievement of college students? A meta-analysis. Journal of Educational Computing Research, 62(7), 1676–1713. https://doi.org/10.1177/07356331241277937
  46. Van Dinther, M., Dochy, F., & Segers, M. (2011).
    Factors affecting students’ self-efficacy in higher education. Educational Research Review, 6(2), 95–108.

    https://doi.org/10.1016/j.edurev.2010.10.003
  47. Williams, R. T. (2024). The ethical implications of using generative chatbots in higher education. Frontiers in Education, 8, 1331607. https://doi.org/10.3389/feduc.2023.1331607
  48. Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in artificial intelligence in education. Learning, Media and Technology, 45(3), 223–235.
    https://doi.org/10.1080/17439884.2020.1798995
  49. Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023).
    A brief overview of ChatGPT: The history, status quo, and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122–1136.

    https://doi.org/10.1109/JAS.2023.123618
  50. Yokoyama, S. (2019). Academic self-efficacy and academic performance in online learning: A mini review. Frontiers in Psychology, 9, 2794.
    https://doi.org/10.3389/fpsyg.2018.02794
  51. Yu, H. (2023). Reflection on whether ChatGPT should be banned by academia from the perspective of education and teaching. Frontiers in Psychology, 14, 1181712.
    https://doi.org/10.3389/fpsyg.2023.1181712
  52. Yu, L., & Yu, Z. (2023).
    Qualitative and quantitative analyses of artificial intelligence ethics in education. Frontiers in Psychology, 14, 1061778.

    https://doi.org/10.3389/fpsyg.2023.1061778
  53. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education Where are the educators? International Journal of Educational Technology in Higher Education, 16, 39.
    https://doi.org/10.1186/s41239-019-0171-0
  54. Zhai, C., & Wibowo, S. (2023). A systematic review on artificial intelligence dialogue systems for enhancing English as a foreign language students’ interactional competence in university learning. Computers and Education: Artificial Intelligence, 4, 100134.
    https://doi.org/10.1016/j.caeai.2023.100134
  55. Zhan, Y., Yan, Z., Wan, Z. H., Wang, X., Zeng, Y., Yang, M., & Yang, L. (2023).
    Effects of online peer assessment on higher-order thinking: A meta-analysis. British Journal of Educational Technology, 54(4), 817–835.

    https://doi.org/10.1111/bjet.13310
  56. Zhang, P., & Tur, G. (2023). A systematic review of ChatGPT use in K-12 education. European Journal of Education, 59(2), e12599.
    https://doi.org/10.1111/ejed.12599
  57. Zhao, X., Cox, A., & Cai, L. (2024). ChatGPT and the digitisation of writing. Humanities and Social Sciences Communications, 11, 482.
    https://doi.org/10.1057/s41599-024-02904-x
  58. Zhong, L. (2022). A systematic review of personalized learning in higher education: Learning content structure, learning materials sequence, and learning readiness support. Interactive Learning Environments.
    https://doi.org/10.1080/10494820.2022.2061006
  59. Zhu, X., Chen, A., Ennis, C., Sun, H., Hopple, C., & Bonello, M. (2009). Situational interest, cognitive engagement, and achievement in physical education. Contemporary Educational Psychology, 34(3), 221–229. https://doi.org/10.1016/j.cedpsych.2009.05.002
  60. Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2

The expeditious evolution of generative artificial intelligence (GenAI) is fundamentally reshaping pedagogy, learning settings, and academic practices within higher education. Despite a proliferation of literature on GenAI in education, the field remains fragmented across technological, pedagogical, and psychological domains. This review synthesizes recent literature (2020–2025), focusing on GenAI's role in supporting academic achievement via AI-mediated digital practices. The analysis specifically examines how key psychological mediators including motivation, cognitive engagement, self-regulated learning, and emotional engagement interact with GenAI use. Furthermore, the paper addresses critical ethical and governance challenges related to GenAI adoption, such as algorithmic bias, academic integrity, data governance, and student privacy. Based on this synthesis, a conceptual framework is proposed to explain how GenAI can effectively promote academic achievement when integrated within ethically responsible and pedagogically sound educational environments.

Keywords : Generative Artificial Intelligence; Academic Achievement; Higher Education; Educational Technology; Student Engagement.

Paper Submission Last Date
30 - April - 2026

SUBMIT YOUR PAPER CALL FOR PAPERS
Video Explanation for Published paper

Never miss an update from Papermashup

Get notified about the latest tutorials and downloads.

Subscribe by Email

Get alerts directly into your inbox after each post and stay updated.
Subscribe
OR

Subscribe by RSS

Add our RSS to your feedreader to get regular updates from us.
Subscribe