Abstracts of Special Funding Scheme for Development of Discipline-specific Artificial Intelligence Courses

(Supported by Teaching Development and Language Enhancement Grant 2022-25)

No.	Project Title	Abstract
1.	Large Language Models for Language-Related Studies	· A new elective course will be developed. The course will be offered to all undergraduate students in the Faculty of Arts. · This course explores the applications of Artificial Intelligence (AI) and Large Language Models (LLMs) in various language-related disciplines (e.g., Linguistics, Chinese, English, Japanese, Translation, Philosophy, Cultural Communication, Psychology) · Students will gain an understanding of the fundamentals of AI, Natural Language Processing (NLP), and the architecture of LLMs · Hands-on sessions using Python will enable students to access, utilize, and fine-tune LLMs for specific language-related tasks · The course covers the use of LLMs in: - Linguistic analysis and computational linguistics research - Language learning, teaching, and assessment - Translation and interpretation - Academic writing and research · Ethical considerations and the societal impact of LLMs in language-related fields will be discussed · Students will explore cognitive and neurological perspectives on LLMs and their comparison to human language processing · The course also addresses future directions and emerging trends in LLMs, such as multimodal models and quantum computing · By the end of the course, students will have a comprehensive understanding of LLMs and their applications in language science; they will be equipped with the skills to harness the power of LLMs for research and practical applications in their respective language-related disciplines
2.	Integrating AI Competency, Ethics, and Innovation into Business Education	Artificial Intelligence (AI) is no longer limited to engineers or data scientists—it is a critical skill for all professionals in the modern business environment. This project aims to develop an AI course for undergraduate students in the Faculty of Business Administration, CUHK. It will equip them with the knowledge and skills to leverage AI in their fields of study and their future work life. The course is application-based, catering the needs of different levels of technical background. The course will help students: 1. Understand fundamental AI concepts and terminology, including machine learning, neural networks, and data science. 2. Identify what AI can and cannot realistically achieve in business settings. 3. Spot opportunities to apply AI to solve organizational challenges. 4. Gain hands-on experience with different AI tools to explore data science and machine learning projects. 5. Navigate ethical, legal, and societal discussions surrounding AI adoption. While the course is application-oriented, it also provides value to those technically competitive students by emphasizing how AI could be translated into business and societal values. It prepares students to become proactive and responsible business leaders in an AI-driven world.
3.	Online Learning and Assessment of AI Competency for School Teachers and Learning Designers	Integration of artificial intelligence (AI) into school education becomes popular. Pre-service teachers and learning designers should be competent to integrate AI in education healthy, ethically, and productively. In this project, we will create a 24-hour online learning course to enhance the AI competency of our CUHK Faculty of Education students. We will design the content and learning tasks of the course based on an AI competency framework for pre-service teachers and learning designers. The framework has six dimensions: AI knowledge, AI pedagogy, AI assessment, AI ethics, human-centered education, and professional engagement. We will also develop an online self-assessment platform, enabling our students to understand their AI competency levels. After the course is completed, our students will be more competent in using AI to improve their learning and performance in the research project, teaching practicum, and internship programs. This course helps students understand and leverage the latest application of AI in education careers.
4.	Online Artificial Intelligence Literacy Workshop for Engineering Students	The Faculty of Engineering plans to develop an online AI literacy workshop which is required for all students in the Faculty, and it will be a non-credit-bearing graduation requirement for all Engineering undergraduates, starting from the cohort of 2025-26. The workshop will comprise 4 micro-modules, including (1) Introduction to AI and its impact to society, (2) Basic Principles in Data Analytics, Machine Learning, Neural Networks and Deep Learning, (3) Major Application Areas in AI: Natural Language Processing, Image Classification and Generative AI, and (4) AI Tools for learning and Social Ethics of AI. Each micro-module will be a video clip of about 15-20 minutes. All the modules will be accessible online. Students are required to complete the online workshop by viewing the provided micro-modules and get a pass in an online quiz, during their first year of study. The aims of these Al literacy micro-modules are to let the freshmen understand the basic concepts of AI, learn the proper use of AI Tools and become aware of the AI ethics. Students are encouraged to make good use of AI tools to enrich their learning activities in their studies.
5.	Responsible Use of AI in Legal Education: From Overreliance to Productivity	AI poses particular challenges for the legal profession: lawyers research, build arguments and, most importantly, consume and produce large amounts of text. There is a clear temptation to deploy AI-based tools to perform those tasks more quickly and seemingly more efficiently. The actual capabilities and limitations of AI are often misunderstood if not altogether disregarded. While students must be equipped with skills enabling them to survive in the increasingly workplace, they must also be trained to use technology in a competent and responsible manner. Bearing in mind that current law students will become lawyers and perform an important function in our society, it is necessary to ensure they become competent and ethical users of AI. The project emphasizes the distinction between different types of AI and different tasks that can be facilitated or automated thereby. AI can bring productivity gains in some tasks but raise significant concerns in others. It is necessary to prevent overreliance, the tendency to blindly trust outputs generated or suggested by AI, preserve the students’ ability to think critically and to increase their overall AI literacy. It is also necessary to engage Faculty members in encouraging and demonstrating the responsible use of AI.
6.	Development of AI Platform to Promote Students' Reflective Capacity in Medical Education	Reflection and reflective writing are critical for professional development and have been essential to learning from experiential learning in Kolb's learning cycle (1984) with 'Reflective observation'. Effective reflection not only involves cognitive reflection on knowledge but also emotional and contextual aspects which help students learn a range of different contexts to enhance meaning - making and relevance across different settings applications. Reflection is important for all disciplines in higher education but particularly in medicine for personal and professional development and identity is constantly formed and honed. A generative AI tool to enhance reflection will be utilized by teachers and students across different courses with the aim enhance pedagogy and integration of AI in reflection and learning. Particularly the application of the tool will be developed for community experiential attachments, clinical hospital attachments and thematically including clinical communication, ethics and professional development. The use of the tool aims to allow students and teachers to integrate AI into teaching and learning and develop pedagogy of AI in the reflection and learning process. Teachers will also gain valued experience of the use and limitations of AI in reflection and feedback. Project experience will be disseminated to faculty teachers and other faculty members and will be a key foundation to further work in use of AI in reflection and AI tools and pedagogy.
7.	Leveraging Generative AI Tools to Enhance Science Students' Professional Communication, Presentation Skills, and Understanding of Key Scientific Concepts	Since the official launch of ChatGPT for public access by OpenAI in November 2022, generative AI tools have had unprecedented and tremendous impacts on virtually all fields of life. By seizing the opportunity to gain appropriate exposure and training in generative AI techniques, students can enhance their competencies and increase their employability in the innovative science sector. As always a significant number of science graduates choose to pursue careers in the education sector or further studies. This highlights the importance of acquiring skills in effective and efficient teaching and training for our undergraduates. By providing students with opportunities and exposure to appropriate generative AI tools, they can leverage these tools to enhance their professional scientific skills and knowledge, thereby elevating their competency to an unprecedented level. The project aims to expose science undergraduate students to various generative AI tools related to multimedia preparation. By leveraging their acquired scientific skills and knowledge, students can enhance their presentation and communication capabilities. Undergraduate students taking elementary-level science courses will be guided to use the appropriate generative AI tools to prepare sets of electronic media presentation materials to explain fundamental science concepts. By engaging in these activities, students will gain exposure to the creative and effective use of generative AI tools, experiencing firsthand how these tools can significantly enhance their subject-specific career competencies. This activity can also deepen students' understanding of essential scientific concepts.
8.	AI for Social Science Research and Study	The first objective of this project is to integrate AI into curriculum, pedagogies and assessment process of two Social Science disciplines (Key Theme 1 (KT1)), namely the Sociology Programme and the Gender Studies Programme. Teaching resources developed will include a series of eight micro modules with accompanying class/take-home activities and assessment case examples, to address the learning need of students at three common yet critical timepoints in their academic journey. These timepoints include: i. Initial Exposure to a Discipline: students will learn about the rules, academic integrity, and responsible use of generative AI at the introductory course level of the discipline (KT4); ii. Learning of Research Methodology: students will discover the potential of AI in assisting research process while becoming aware of its pitfalls and limitation during methodological training in both qualitative and quantitative method courses (KT2, 3); iii. Capstone Projects: students will further cultivate the ability to make sound judgments about the quality of generative AI output while completing capstone projects, which usually synthesize and apply discipline-based knowledge (KT2, 3, 4). By graduation, students would gain the confidence to, metaphorically speaking, “drive” AI and be ready for a AI-mediated career. The eight micro-modules will be designed with a generic social science focus to allows them to: 1) be flexibly adapted for use in introductory courses, methodology courses, and/or capstone courses across various disciplines, and; 2) serve as the backbone of a potential one-credit course for entry-level social science students. To align with CUHK’s diversity and inclusion principles, the secondary objective of this project is to develop and design accessible teaching and learning resources that take into consideration how AI can serve as assistive technology tools for students with special learning need – with a focus on ways that AI can enable higher order cognitive process among SEN and non-SEN students.