AI-Enhanced Inclusive Pedagogy: A Case Study of Automatic Feedback in a Diverse Classroom

Man Ching Mary Cheng

Research output: Unpublished conference presentation (presented paper, abstract, poster)Conference presentation (not published in journal/proceeding/book)Teaching and learningpeer-review

Abstract

Due to the diversity of classrooms, educators face the challenge of making instruction equal and effective for students with unique needs (Du Plessis, 2019). The incorporation of AI-driven tools into pedagogical practices may address this challenge. Prior studies suggest that AI-generated feedback can assist language learners by providing prompt, precise, and unambiguous responses (Escalante et al., 2023; Lee, 2023). The current study investigates the influence of an AI-powered feedback system on the educational experiences and perceived academic development of undergraduate students at a university in Hong Kong. The targeted demographic included students from diverse cultural backgrounds with different expectations of higher education; students displaying a range of academic abilities; and students requiring specialized educational support. Data collection was performed by using qualitative research tools, including students' reflective journals, a survey questionnaire, personal interviews, assignment evaluations, and observations from teachers. Results indicate that the feedback generated by AI offers individual support and significantly enhances student motivation. The system responds to the learning needs of individual students in an overcoming-of-language-barriers and proficiency-level-friendly manner. It contributes to the ongoing discussion related to technology-enhanced inclusive pedagogy.
References:
Du Plessis, A. E. (2019). Barriers to effective management of diversity in classroom contexts: The out-of-field teaching phenomenon. International Journal of Educational Research, 93, 136-152.
Escalante, J., Pack, A., & Barrett, A. (2023). AI-generated feedback on writing: insights into efficacy and ENL student preference. International Journal of Educational Technology in Higher Education, 20(1), 57.
Lee, A. V. Y. (2023). Supporting students’ generation of feedback in large-scale online course with artificial intelligence-enabled evaluation. Studies in Educational Evaluation, 77, 101250.
Original languageEnglish
Publication statusNot published / presented only - 2 Jul 2025
Event21st ISATT Biennial Conference - University of Glasgow, Glasgow, United Kingdom
Duration: 30 Jun 20254 Jul 2025
https://www.gla.ac.uk/events/conferences/isatt2025/

Conference

Conference21st ISATT Biennial Conference
Country/TerritoryUnited Kingdom
CityGlasgow
Period30/06/254/07/25
Internet address

Keywords

  • GenAI, AI, academic writing, process writing

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