ISSN: 2226-6348
Open access
This study has delved into exploring the impact of an AI-based dynamic feedback system on student academic performance in higher education, particularly in the context of China. Accordingly, this research is carried out with the integration of “perceived ease of use”, “perceived usefulness”, “feedback”, and “motivation”, on “student academic performance”, which are grounded in the Technology Acceptance Model (TAM), Self-Regulated Learning Theory, and Constructivist Learning Theory. The integration of these constructs has encouraged the progress of the research in a systematic manner by developing relevant objectives, questions, and hypotheses employing the same. A quantitative research design has been adopted, with data collected from 420 undergraduate and postgraduate students in Guangdong, China, using a structured questionnaire based on a five-point Likert scale. The findings underscore that AI-based feedback systems positively influence student academic performance by providing timely, personalised, and actionable insights that foster engagement, motivation, and self-regulated learning, ultimately enhancing their academic performance. The outcomes from the descriptive analysis revealed a higher degree of agreement regarding the significance of the constructs on perceptions of usefulness, ease of use, feedback, and motivation. Reliability and validity tests have further added to the conformity of strong internal consistency and significant positive correlations between all independent variables and student academic performance.
Acosta-Gonzaga, E. (2023). The effects of self-esteem and academic engagement on university students’ performance. Behavioral Sciences, 13(4), 348. https://doi.org/10.3390/bs13040348
Acosta-Gonzaga, E., & Ramirez-Arellano, A. (2021). The influence of motivation, emotions, cognition, and metacognition on students’ learning performance: A comparative study in higher education in blended and traditional contexts. Sage Open, 11(2), 21582440211027561. https://doi.org/10.1177/21582440211027561
Ahmed, M., Thomas, M., & Farooq, R. (2021). The impact of teacher feedback on students’ academic performance: A mediating role of self-efficacy. Journal of Development and Social Sciences, 2(3), 464-480.https://jdss.org.pk/article/the-impact-of-teacher-feedback-on-students-academic-performance-a-mediating-role-of-self-efficacy
Al Rawashdeh, A. Z., Mohammed, E. Y., Al Arab, A. R., Alara, M., & Al-Rawashdeh, B. (2021). Advantages and disadvantages of using e-learning in university education: Analyzing students’ perspectives. Electronic Journal of E-learning, 19(3), 107-117.https://academic-publishing.org/index.php/ejel/article/view/2168
Al-Abdullatif, A. M., & Gameil, A. A. (2021). The Effect of Digital Technology Integration on Students' Academic Performance through Project-Based Learning in an E-Learning Environment. International Journal of Emerging Technologies in Learning, 16(11). https://doi.org/10.3991/ijet.v16i11.19421
Alam, M. M., Ahmad, N., Naveed, Q. N., Patel, A., Abohashrh, M., & Khaleel, M. A. (2021). E-learning services to achieve sustainable learning and academic performance: An empirical study. Sustainability, 13(5), 2653. https://doi.org/10.3390/su13052653
Al-Bashayreh, M., Almajali, D., Altamimi, A., Masa’deh, R. E., & Al-Okaily, M. (2022). An empirical investigation of reasons influencing student acceptance and rejection of mobile learning apps usage. Sustainability, 14(7), 4325. https://doi.org/10.3390/su14074325
Albreiki, B., Zaki, N., & Alashwal, H. (2021). A systematic literature review of student’performance prediction using machine learning techniques. Education Sciences, 11(9), 552. https://doi.org/10.3390/educsci11090552
Al-Mamary, Y. H. S. (2022). Why do students adopt and use learning management systems?: Insights from Saudi Arabia. International Journal of Information Management Data Insights, 2(2), 100088.https://www.sciencedirect.com/science/article/pii/S2667096822000313
Badr, A. M., Al-Abdi, B. S., Rfeqallah, M., Kasim, R., & Ali, F. A. (2024). Information quality and students’ academic performance: the mediating roles of perceived usefulness, entertainment and social media usage. Smart Learning Environments, 11(1), 45.https://link.springer.com/article/10.1186/s40561-024-00329-2
Chiu, T. K., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2024). Teacher support and student motivation to learn with Artificial Intelligence (AI) based chatbot. Interactive Learning Environments, 32(7), 3240-3256. https://doi.org/10.1080/10494820.2023.2172044
Conrad, C., Deng, Q., Caron, I., Shkurska, O., Skerrett, P., & Sundararajan, B. (2022). How student perceptions about online learning difficulty influenced their satisfaction during Canada's Covid?19 response. British Journal of Educational Technology, 53(3), 534-557.https://bera-journals.onlinelibrary.wiley.com/doi/abs/10.1111/bjet.13206
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). 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
Fisher, D. P., Brotto, G., Lim, I., & Southam, C. (2025). The impact of timely formative feedback on university student motivation. Assessment & Evaluation in Higher Education, 1-10.https://www.tandfonline.com/doi/abs/10.1080/02602938.2025.2449891
Fülöp, M. T., Breaz, T. O., Topor, I. D., Ionescu, C. A., & Dragolea, L. L. (2023). Challenges and perceptions of e-learning for educational sustainability in the “new normality era”. Frontiers in Psychology, 14, 1104633. https://doi.org/10.3389/fpsyg.2023.1104633
Gan, Z., An, Z., & Liu, F. (2021). Teacher feedback practices, student feedback motivation, and feedback behavior: how are they associated with learning outcomes?. Frontiers in psychology, 12, 697045. https://doi.org/10.3389/fpsyg.2021.697045
Garbuio, M., & Lin, N. (2021). Innovative idea generation in problem finding: Abductive reasoning, cognitive impediments, and the promise of artificial intelligence. Journal of Product Innovation Management, 38(6), 701-725.https://onlinelibrary.wiley.com/doi/abs/10.1111/jpim.12602
García-Martínez, I., Fernández-Batanero, J. M., Fernández-Cerero, J., & León, S. P. (2023). Analysing the impact of artificial intelligence and computational sciences on student performance: Systematic review and meta-analysis. Journal of New Approaches in Educational Research, 12(1), 171-197. https://doi.org/10.7821/naer.2023.1.1240
Gardner, J., O'Leary, M., & Yuan, L. (2021). Artificial intelligence in educational assessment:‘Breakthrough? Or buncombe and ballyhoo?’. Journal of Computer Assisted Learning, 37(5), 1207-1216.https://onlinelibrary.wiley.com/doi/abs/10.1111/jcal.12577
Ghanad, A. (2023). An overview of quantitative research methods. International journal of multidisciplinary research and analysis, 6(08), 3794-3803.https://doi.org/10.47191/ijmra/v6-i8-52
Hanham, J., Lee, C. B., & Teo, T. (2021). The influence of technology acceptance, academic self-efficacy, and gender on academic achievement through online tutoring. Computers & Education, 172, 104252.https://www.sciencedirect.com/science/article/pii/S0360131521001299
Hongsuchon, T., Emary, I. M. E., Hariguna, T., & Qhal, E. M. A. (2022). Assessing the impact of online-learning effectiveness and benefits in knowledge management, the antecedent of online-learning strategies and motivations: An empirical study. Sustainability, 14(5), 2570. https://doi.org/10.3390/su14052570
Hsiao, P. W., & Su, C. H. (2021). A study on the impact of STEAM education for sustainable development courses and its effects on student motivation and learning. Sustainability, 13(7), 3772. https://doi.org/10.3390/su13073772
Ismail, S. M., Rahul, D. R., Patra, I., & Rezvani, E. (2022). Formative vs. summative assessment: impacts on academic motivation, attitude toward learning, test anxiety, and self-regulation skill. Language Testing in Asia, 12(1), 40. https://doi.org/10.1186/s40468-022-00191-4
Katajisto, M., Uusiautti, S., & Hyvärinen, S. (2023). “The Best Thing Was to Realize that I Am Not a Nobody. I Am Meaningful.”: Students’ Perceptions of a Strengths-Based Approach to Guidance. International Journal of Educational Psychology, 12(1), 92-118.https://research.ulapland.fi/fi/publications/the-best-thing-was-to-realize-that-i-am-not-a-nobody-i-am-meaning
Klein, P., Ivanjek, L., Dahlkemper, M. N., Jeli?i?, K., Geyer, M. A., Küchemann, S., & Susac, A. (2021). Studying physics during the COVID-19 pandemic: Student assessments of learning achievement, perceived effectiveness of online recitations, and online laboratories. Physical review physics education research, 17(1), 010117.https://journals.aps.org/prper/abstract/10.1103/PhysRevPhysEducRes.17.010117
Koziko?lu, ?., & Albayrak, E. N. (2022). Teachers' attitudes and the challenges they experience concerning individualized education program (IEP): A mixed method study. Participatory Educational Research, 9(1), 98-115.https://dergipark.org.tr/en/doi/10.17275/per.22.6.9.1
Leitão, R., Maguire, M., Turner, S., & Guimarães, L. (2022). A systematic evaluation of game elements effects on students’ motivation. Education and Information Technologies, 27(1), 1081-1103. https://doi.org/10.1007/s10639-021-10651-8
Lim, L. A., Gentili, S., Pardo, A., Kovanovi?, V., Whitelock-Wainwright, A., Gaševi?, D., & Dawson, S. (2021). What changes, and for whom? A study of the impact of learning analytics-based process feedback in a large course. Learning and Instruction, 72, 101202.https://www.sciencedirect.com/science/article/pii/S095947521830450X
Lin, Y., & Yu, Z. (2023). Extending Technology Acceptance Model to higher-education students’ use of digital academic reading tools on computers. International Journal of Educational Technology in Higher Education, 20(1), 34. https://doi.org/10.1186/s41239-023-00403-8
Mackinney, R., Kelly, J., & Pulling, C. (2021). The effect of feedback type on academic performance. Practice and Evidence of the Scholarship of Teaching and Learning in Higher Education, 15(1), 78-93.https://www.pestlhe.org/index.php/pestlhe/article/view/231
Malureanu, A., Panisoara, G., & Lazar, I. (2021). The relationship between self-confidence, self-efficacy, grit, usefulness, and ease of use of elearning platforms in corporate training during the COVID-19 pandemic. Sustainability, 13(12), 6633. https://doi.org/10.3390/su13126633
Mazhar, S. A., Anjum, R., Anwar, A. I., & Khan, A. A. (2021). Methods of data collection: A fundamental tool of research. Journal of Integrated Community Health, 10(1), 6-10.DOI: 10.24321/2319.9113.202101
Morris, R., Perry, T., & Wardle, L. (2021). Formative assessment and feedback for learning in higher education: A systematic review. Review of Education, 9(3), e3292. https://doi.org/10.1002/rev3.3292
Morris, R., Perry, T., & Wardle, L. (2021). Formative assessment and feedback for learning in higher education: A systematic review. Review of Education, 9(3), e3292.https://bera-journals.onlinelibrary.wiley.com/doi/abs/10.1002/rev3.3292
Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, Á. (2023). Human-in-the-loop machine learning: a state of the art. Artificial Intelligence Review, 56(4), 3005-3054.https://link.springer.com/article/10.1007/s10462-022-10246-w
Muchai, A. M., & Ng’asike, J. T. (2021). Influence of Children Safety on Delivery of Early Childhood Education, Mwatate Sub County, Taita Taveta County, Kenya. American Journal of Educational Research, 9(2), 77-82.doi:10.12691/education-9-2-4
Muñoz-Carril, P. C., Hernández-Sellés, N., Fuentes-Abeledo, E. J., & González-Sanmamed, M. (2021). Factors influencing students’ perceived impact of learning and satisfaction in Computer Supported Collaborative Learning. Computers & Education, 174, 104310. https://doi.org/10.1016/j.compedu.2021.104310
Rahman, A., & Muktadir, M. G. (2021). SPSS: An imperative quantitative data analysis tool for social science research. International Journal of Research and Innovation in Social Science, 5(10), 300-302.https://www.academia.edu/download/79116055/300-302.pdf
Romero-Rodríguez, J. M., Ramírez-Montoya, M. S., Buenestado-Fernández, M., & Lara-Lara, F. (2023). Use of ChatGPT at university as a tool for complex thinking: Students’ perceived usefulness. Journal of New Approaches in Educational Research, 12(2), 323-339. https://doi.org/10.7821/naer.2023.7.1458
Saleela, D., Oyegoke, A. S., Dauda, J. A., & Ajayi, S. O. (2025). Development of AI-Driven decision support system for personalized housing adaptations and assistive technology. Journal of Aging and Environment, 1-24.https://www.tandfonline.com/doi/abs/10.1080/26892618.2025.2534956
Smith, J., Guimond, F. A., Bergeron, J., St-Amand, J., Fitzpatrick, C., & Gagnon, M. (2021). Changes in students’ achievement motivation in the context of the COVID-19 pandemic: A function of extraversion/introversion?. Education Sciences, 11(1), 30. https://doi.org/10.3390/educsci11010030
Taherdoost, H. (2021). Data collection methods and tools for research; a step-by-step guide to choose data collection technique for academic and business research projects. International Journal of Academic Research in Management (IJARM), 10(1), 10-38.https://hal.science/Hal-03741847/
Tan, J., Mao, J., Jiang, Y., & Gao, M. (2021). The influence of academic emotions on learning effects: A systematic review. International journal of environmental research and public health, 18(18), 9678. https://doi.org/10.3390/ijerph18189678
Tanujaya, B., Prahmana, R. C. I., & Mumu, J. (2022). Likert scale in social sciences research: Problems and difficulties. FWU Journal of Social Sciences, 16(4), 89-101.http://doi.org/10.51709/19951272/Winter2022/7
Textor, C. (2024). China: number of undergraduate students in major cities. Statista. https://www.statista.com/statistics/1182360/china-number-of-undergraduates-at-institutions-of-higher-education-in-major-cities/Textor, C. (2024). China: number of undergraduate students in major cities. Statista. https://www.statista.com/statistics/1182360/china-number-of-undergraduates-at-institutions-of-higher-education-in-major-cities/
Urhahne, D., & Wijnia, L. (2023). Theories of motivation in education: An integrative framework. Educational Psychology Review, 35(2), 45. https://doi.org/10.1007/s10648-023-09767-9
Wei, L. (2023). Artificial intelligence in language instruction: impact on English learning achievement, L2 motivation, and self-regulated learning. Frontiers in psychology, 14, 1261955. https://doi.org/10.3389/fpsyg.2023.1261955
Zou, M., & Huang, L. (2023). To use or not to use? Understanding doctoral students’ acceptance of ChatGPT in writing through technology acceptance model. Frontiers in Psychology, 14, 1259531. https://doi.org/10.3389/fpsyg.2023.1259531
Ying, Z., Nasri, M. N. (2025). The Impact of an Ai-Based Dynamic Feedback System on Student Academic Performance in Higher Education. International Journal of Academic Research in Progressive Education and Development, 14(4), 1467-1485.
Copyright: © 2025 The Author(s)
Published by HRMARS (www.hrmars.com)
This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at: http://creativecommons.org/licences/by/4.0/legalcode