This study explores the dual impact of Artificial Intelligence-Generated Content (AIGC) technology on college students’ learning efficiency and motivation. Using a qualitative, interview-based approach, this research collected data from 10 students across various disciplines and academic years to assess both the benefits and challenges of AIGC use in academic contexts. The findings reveal that AIGC tools significantly enhance learning efficiency for most students (90%), primarily by reducing cognitive load and facilitating faster task completion. This efficiency gain allows students to focus more on higher-order cognitive tasks, such as critical analysis and creative problem-solving. However, the impact on learning motivation is mixed. While 60% of participants reported increased motivation due to reduced cognitive effort, 40% expressed concerns about over-reliance on automated outputs potentially undermining critical thinking and deep learning. Additionally, ethical and pedagogical challenges emerged as critical concerns, with 40% of students highlighting risks related to academic integrity and the potential for reduced independent thinking. These findings underscore the need for balanced integration of AIGC technology in educational settings, emphasizing the importance of digital literacy to mitigate the risks of over-dependence. This study contributes to the growing body of literature on educational technology, providing valuable insights for educators, policymakers, and technology developers seeking to optimize the use of AIGC in academic environments.

You Chen, Shuai Wu, Jiewen Chen, Xiaobang Wu. The Dual Impact of AIGC Technology on College Students’Learning Efficiency and Motivation: An Interview-Based Study. International Journal of New Developments in Education (2025), Vol. 7, Issue 5: 56-64. https://doi.org/10.25236/IJNDE.2025.070509.

[1] Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., Bin Saleh, K., ... & Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in social and administrative pharmacy, 19(8), 1236-1242.

[2] Zhu, Y., & Yang, F. (2023). ChatGPT/AIGC and educational innovation: Opportunities, challenges, and the future. Journal of East China Normal University (Educational Sciences), 41(7), 1-14.

[3] Hattenhauer, R. (2024). ChatGPT & Co.: A workbook for writing, research, creating images, programming, and more. CRC Press.

[4] Tang, Y. Y. (2024). Application of the AIGC large language model in college English writing teaching. Chinese Language and Literature, 3(1), 10-15.

[5] Liu, M., Zhang, L. J., & Biebricher, C. (2024). Investigating students’ cognitive processes in generative AI-assisted digital multimodal composing and traditional writing. Computers and Education, 211, 104977.

[6] Cheng, Y., & Wang, T. (2024, November). The impact of AIGC on modern education: Transforming learning and teaching methods. In 2024 International conference on intelligent education and intelligent research (IEIR) (pp. 1-6). IEEE.

[7] Zou, X., Su, P., Li, L., & Fu, P. (2024). AI-generated content tools and students’ critical thinking: Insights from a Chinese university. IFLA Journal, 50(2), 228-241.

[8] Tica, L., & Krsmanović, I. (2024). Overcoming the writer’s block? Exploring students’ motivation and perspectives on using ChatGPT as a writing assistance tool in ESP. ELOPE: English Language Overseas Perspectives and Enquiries, 21(1), 129-149.

[9] Khan, A. S., & Khan, A. (2024). Overcoming writing challenges: The use of Ai-Powered writing assistants and online collaboration tools in foreign language education. Journal of International Crisis and Risk Communication Research, 7(2), 314-326.

[10] Bermeo, S. D. R., Salazar, M. L., Terán, F. F. P., Calero, J. C. M., & Centeno, V. J. A. (2024). Impact of AI on virtual learning: Self-sufficiency and academic confidence in university students. Ciencia Latina Revista Científica Multidisciplinar, 8(6), 7414-7429.

[11] Shah, S. S., & Asad, M. M. (2024). Impact of critical thinking approach on learners’ dependence on innovative transformation through artificial intelligence. In The evolution of artificial intelligence in higher education: Challenges, risks, and ethical considerations (pp. 161-182). Emerald Publishing Limited.

[12] Aoun, J. E. (2017). Robot-proof: higher education in the age of artificial intelligence. MIT press.

[13] Wang, D., Liu, Y., Jing, X., Liu, Q., & Lu, Q. (2024). Catalyst for future education: An empirical study on the Impact of artificial intelligence generated content on college students’ innovation ability and autonomous learning. Education and Information Technologies, 1-20.

[14] Huang, K. L., Liu, Y. C., & Dong, M. Q. (2024). Incorporating AIGC into design ideation: A study on self-efficacy and learning experience acceptance under higher-order thinking. Thinking Skills and Creativity, 52, 101508.

[15] Guo, D., Chen, H., Wu, R., & Wang, Y. (2023). AIGC challenges and opportunities related to public safety: a case study of ChatGPT. Journal of Safety Science and Resilience, 4(4), 329-339.

[16] Jose, J., & Jose, B. J. (2024). Educators’ academic insights on artificial intelligence: challenges and opportunities. Electronic Journal of E-Learning, 22(2), 59-77.

[17] Kenedy, R. A. (2024). The challenges of critical thinking in the era of artificial intelligence. European Journal of Multidisciplinary Studies, 9(2), 19-36.

[18] Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 1-28.

[19] Zhang, S., Li, H., Sun, K., Chen, H., Wang, Y., & Li, S. (2025). Security and privacy challenges of AIGC in metaverse: A comprehensive survey. ACM Computing Surveys, 57(10),1-37.

[20] Su, N., Wang, Y., Ye, C., & Liu, Y. (2024). Artificial Intelligence in General Context (AIGC) technology’s impact on higher education Pedagogical Content Knowledge (PCK) teaching. Advances in Education, Humanities and Social Science Research, 12(1), 8311.