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Support–Guide–Release: Construction and Practice of a Three-Stage Progressive AI-Empowered Teaching Model for Research Methods Courses

Author(s)

Lu Xiang

Affiliation(s)

Zhaoqing University, Zhaoqing, Guangdong, China

Abstract

With the fast growth of generative artificial intelligence (GenAI) come new challenges and opportunities for higher education. To address the tension between the powerful content-generation ability of GenAI and the cultivation of students' independent research ability, and supported by the zone of proximal development theory, cognitive load theory, and human–machine collaboration theory, this study constructs an AI-empowered, three-stage progressive teaching model (the "Support–Guide–Release" model), and completes a full round of teaching practice with 173 students enrolled in two research methods courses. The paper systematically explains the logic of AI's role at the three stages: initial topic exploration, literature review optimization, and research design, and presents three core design principles: limited assistance, authentic data bottom line, and progressive empowerment. For each stage, concrete examples of instructional prompts are also given. Based on the feedback from practice, the combination of stage-by-stage scaffolding is able to effectively alleviate the learners' initial cognitive load; the "independent work first, then AI" process design can effectively enhance the self-correction ability of students; the authentic-data bottom line can effectively enhance students' identification with research norms. In response to the problems that have been revealed when putting this into practice—namely, cognitive-load overload, the lack of criteria for evaluating AI use, and superficial disclosure—this paper proposes improvement strategies and an evaluation scale, and hopes to provide an actionable practical example for the teaching reform of research methods courses.

Keywords

Generative Artificial Intelligence; Research Methods Courses; Human–Machine Collaborative Teaching; Scaffolding Theory; Teaching Model

References

[1] LO C K. What is the impact of ChatGPT on education? A rapid review of the literature. Education Sciences, 2023, 13(4):410. [2] DEMPERE J, MODUGU K, HESHAM A, et al. The impact of ChatGPT on higher education. Frontiers in Education, 2023, 8:1206936. [3] ANSARI A N, AHMAD S, BHUTTA S M. Mapping the global evidence around the use of ChatGPT in higher education: A systematic scoping review. Education and Information Technologies, 2024, 29(9):11281-11321. [4] BATISTA J, MESQUITA A, CARNAZ G. Generative AI and higher education: Trends, challenges, and future directions from a systematic literature review. Information, 2024, 15(11):676. [5] KASNECI E, SEßLER K, KÜCHEMANN S, et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 2023, 103:102274. [6] DILLENBOURG P. The evolution of research on digital education. International Journal of Artificial Intelligence in Education, 2016, 26(2):544-560. [7] ZHU ZHITIAN, DAI LING, HU JIAO. Higher Consciousness Generative Learning: Innovation of Learning Paradigm Enabled by AIGC Technology. E-Education Research, 2023, 44(6):5-14. [8] YU SHENGQUAN, WANG FANZONG. Cognitive Outsourcing Pitfalls of Artificial Intelligence for Educational Applications and Their Crossing. E-Education Research, 2023, 44(12):5-13. [9] ZHU YONGXIN, YANG FAN. ChatGPT/AIGC and Educational Innovation: Opportunities, Challenges, and the Future. Journal of East China Normal University (Education Sciences), 2023, 41(7): 1-14. [10]MOLENAAR I. Towards hybrid human-AI learning technologies. European Journal of Education, 2022, 57(4):632-645. [11]CUKUROVA M. The interplay of learning, analytics and artificial intelligence in education: A vision for hybrid intelligence. British Journal of Educational Technology, 2025, 56(2):469-488. [12]KALDARAS L, WANG K D, NARDO J E, et al. Employing technology-enhanced feedback and scaffolding to support the development of deep science understanding using computer simulations. International Journal of STEM Education, 2024, 11(1):30. [13]POLLY D, BYKER E. Considering the role of zone of proximal development and constructivism in supporting pre-service teachers' integration of technology. Revista de Educación a Distancia, 2020, 20(64):6. [14]SWELLER J. Cognitive load theory and educational technology. Educational Technology Research and Development, 2020, 68(1):1-16. [15]SKULMOWSKI A, XU K M. Understanding cognitive load in digital and online learning: A new perspective on extraneous cognitive load. Educational Psychology Review, 2022, 34(1):171-196.