Muhammad Ali

The University of Hong Kong, Faculty of Education, Postgraduate Student

The University of Hong Kong, Department of Biomedical Engineering and Data Science, Undergraduate

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Muhammad Ali is a PhD candidate at the Faculty of Education at the University of Hong Kong. He is an engineer by training, holding degrees in Biomedical Engineering and Data Science (BEng) as well as STEM Education (MEd). Before entering academia, he worked as a software engineer and developer, eventually becoming an advisory manager at a leading EdTech startup specializing in robotics and automation. He also founded an educational initiative to facilitate the integration of advanced computational technologies within K–12 educational settings. Drawing on this diverse experience, his current research focuses on enhancing computational thinking and AI literacy among K–12 teachers.
Supervisors: Prof. Gary K.W. Wong, Prof. Daniel Churchill, and Dr. Alex W.C. Tse

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Papers by Muhammad Ali

Optimizing Directional Stimulus Prompting Through Human Feedback: A Structured Approach to AI-Powered Scaffolding

Proceedings of the 2025 Centre for Information Technology in Education Research Symposium (CITERS), 2025

This paper introduces Optimizing Directional Stimulus Prompting Through Human Feedback (oDSP-HF),... more This paper introduces Optimizing Directional Stimulus Prompting Through Human Feedback (oDSP-HF), a structured approach to AI-powered scaffolding that enhances LLM-driven educational support. By refining ‘Directional Stimulus Prompting’ (DSP) through user interaction, oDSP-HF enables LLMs to generate adaptive, reflective hints rather than direct answers. This approach was applied in the system prompting of two AI agents—Aiza and Alice, designed to support Academic English writing and computational thinking, respectively—demonstrating its practical applications in education.

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Exploring Primary School Teachers' Perspectives in Integrating AI into STEM Education through Modular STEM Activities

by Pui Yui Tam and Muhammad Ali

Proceedings of the 9th International Conference on Computational Thinking and STEM Education (CTE-STEM 2025), 2025

This study explores the integration of AI education into a Hong Kong primary school through modul... more This study explores the integration of AI education into a Hong Kong primary school through modular STEM activities, addressing challenges related to teacher preparedness and curriculum design. Collaborating with three STEM teachers, we co-developed hands-on learning modules delivered via an after-school club. Qualitative analysis of observations, interviews, and learning artifacts revealed teachers' initial conceptual and technical struggles, which were mitigated by iterative co-design and low-code tools. While modular resources lowered entry barriers, dependency risks emerged, particularly among novice educators relying on predefined learning content. Experienced teachers demonstrated adaptive innovation, repurposing STEM pedagogies for AI contexts. Findings emphasize the dual role of modular STEM activities-standardizing implementation while enabling flexibility for skilled educators. Furthermore, the findings underscore the need for structured teacher professional development (TPD) that provides scaffolded support to promote autonomy among in-service teachers, fostering critical AI literacy and pedagogical agency.

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Developing Alice: A Scaffolding Agent for AI-Mediated Computational Thinking

by Muhammad Ali, Bixia Chen, and Gary Wong

Proceedings of the 9th International Conference on Computational Thinking and STEM Education (CTE-STEM 2025), 2025

Recent advances in generative AI have increasingly highlighted the transformative potential of la... more Recent advances in generative AI have increasingly highlighted the transformative potential of large language models (LLMs) within educational contexts. Nevertheless, the token-based generation characteristic of LLMs often results in responses that may lack depth, thereby potentially limiting their effectiveness as scaffolding tools. This paper introduces Alice, a scaffolding agent designed to provide unsolicited hints and adaptive support in computational thinking (CT) education. Alice's effectiveness was primarily evaluated through user feedback scores and benchmarked programming tasks, while further empirical research is underway to explore qualitative evidence of scaffolding effectiveness. Alice was initially optimized for both plugged and unplugged learning scenarios using a structured system prompt informed by a hierarchical framework for AI-mediated CT and the oDSP-HF approach. Subsequent fine-tuning with a LoRA-based method reduced perplexity from 9.5 to 6.6 and improved JavaScript-to-block-based code conversion accuracy from 45.8% to 69.5%. User ratings also increased from 64% to 85%. These findings tentatively indicate that careful system prompt design, combined with targeted fine-tuning, may enhance the adaptive support and learner engagement provided by LLM-based scaffolding agents in CT education.

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K-12 Pre-service Teachers' Perspectives on AI Models and Computational Thinking: The Insights from an Interpretative Research Inquiry

Proceedings of the 8th APSCE International Conference on Computational Thinking and STEM Education (CTE-STEM 2024), 2024

Computational thinking (CT) has emerged as a pivotal component of K–12 education for fostering pr... more Computational thinking (CT) has emerged as a pivotal component of K–12 education for fostering problem-solving skills among the next generation of learners. However, CT integration remains an arduous challenge for K–12 teachers due to their limited preparation, prior knowledge, and relevant expertise in CT. To respond to this challenge in Hong Kong, we designed and implemented an introductory CT course employing plugged and unplugged CT approaches alongside AI technology to prepare pre-service teachers. To inform the design of our future course iterations, we conducted an interpretative research inquiry within the course to explore how these teacher trainees learn CT through different teaching and learning activities. Our data analysis accentuated the emergence of three core themes, encompassing numerous subthemes within our data. The three core themes are delineated as themes of (1) CT Knowledge, (2) CT Perspectives, and (3) Potential Barriers. This paper disseminates part of our findings on the trainees' CT Perspectives only: It delves into their post-course perspectives on AI models and CT, seeking to elucidate the pedagogical implications of integrating AI models and CT into K–12 education. These perspectives provide new insights into teaching and learning CT through prompt engineering, which could emerge as a novel approach to democratizing CT education and could be the conduit to bridge the divide between CT and general education.

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Research Trends and Issues of Engineering Design Process for STEM Education in K-12: A Bibliometric Analysis

International Journal of Education in Mathematics, Science, and Technology (IJEMST), 2023

The study performed a bibliometric analysis on research literature related to 'engineering design... more The study performed a bibliometric analysis on research literature related to 'engineering design process' (EDP) that has emerged as a popular approach for STEM education in K-12. The literature comprised 142 journal articles published from 2011 to 2021. There are three objectives of the study. Firstly, to identify the leading research trends of EDP for STEM education that have developed since the release of A Framework for K-12 Science Education in 2011. This framework is pivotal as it paved the way for the establishment of the Next Generation Science Standards in the United States. Secondly, to discern possible research issues on the aforesaid topic by analyzing the research trends. Lastly, to identify publications and authors that have generated prominent citation impact. Since EDP is an emergent approach for STEM education, fulfilling these three objectives can be conducive in facilitating future researchers to build upon the past foundation of research. In this study, the bibliometric data was identified and exported from Web of Science Core Collection: a database with rich scientific literature. The results identified major research trends and issues on EDP pertaining to professional development, design thinking and computational thinking, STEM competencies, scientific inquiry, and gender gaps in STEM education.

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State of STEM Education in Hong Kong: A Policy Review

Academia Letters, 2021

The paper reviewed the state of STEM education in Hong Kong from a policy perspective. It identif... more The paper reviewed the state of STEM education in Hong Kong from a policy perspective. It identified several key issues pertaining to its implementation progress, especially at the secondary school-level. Most notably, the lack of efforts for STEM integration of the Key Learning Areas (KLAs) across science, technology, engineering, and mathematics education; and limited professional development opportunities for K-12 teachers. In addition, the paper briefly discussed possible recommendations to mitigate these issues which can be conducive to facilitate future research studies addressing this topic.

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