Xin Xiang LIM
Department of Biological Sciences,
Faculty of Science, NUS
Lim, X. X. (2024). Harnessing generative AI as a personalised tutor: Enhancing interdisciplinary learning outcomes for biotechnology graduate students [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-xxlim/
SUB-THEME
Opportunities from Generative AI
KEYWORDS
ChatGPT, Student’s Prompt Analysis, Interdisciplinary learning, Generative AI, Personalised Tutor
CATEGORY
Lightning Talk
EXTENDED ABSTRACT
The escalating complexity of societal issues necessitates that graduates from higher educational institutions engage in problem-solving endeavours that transcend singular disciplines (Mansilla & Duraising, 2007; Repko., 2007). When students grasp and interconnect a diverse array of knowledge and skills, their educational experiences become more fulfilling, and their employment prospects broaden (Ivanitskaya & Montgomery., 2002). This phenomenon is particularly pertinent in biotechnology, where innovation is paramount in transforming laboratory discoveries into marketable products that address societal challenges. The innovation process extends beyond biology, requiring an understanding of market needs, funding sources, business models, risk management, and competitor analysis—critical real-world considerations. Thus, fostering innovation demands collaborative, interdisciplinary approaches to facilitate the cross-pollination of ideas and the integration of multiple perspectives. Equipping students with the skills to identify problems, develop prototypes, and conduct market research can provide a robust framework for innovation (Boms et al., 2022).
Pharmaceutical sciences, chemistry, and biotechnology, typically have limited exposure to essential business considerations necessary for evaluating product market viability. This gap underscores the need for substantial scaffolding in business and innovation concepts to enable students to integrate their biotechnological expertise with business innovation, ultimately facilitating product creation.
Generative AI offers significant potential to enrich learning experiences, fostering creativity, critical thinking, and motivation among students. ChatGPT, for instance, has demonstrated efficacy in enhancing interactive learning and personalised tutoring (Baidoo-Anu & Ansah., 2023). Leveraging ChatGPT promotes inquiry-based learning and student-centric approaches, both of which are effective in enhancing learning outcomes. Previous studies indicate that generative AI tools increase intrinsic motivation, conversational engagement, and continuous idea expression among students (Ryan & Deci., 2020) This context presents two educational opportunities: 1) inquiry-based active learning through prompt generation (prompt engineering), and 2) learning from generative AI responses. Developing writing prompts and prompting strategies has become a critical skill in handling generative AI. Prompt datasets collected from generative AI, including log data, can capture students’ learning processes in non-invasive ways. Prompt analysis thus provides an opportunity for educators to gain insights into students’ perceptions, motivations, and behaviors concerning interdisciplinary learning.
Evaluation of students’ interdisciplinary learning outcomes will be conducted through three primary methods: 1) pre- and post-course surveys to capture students’ self-perceptions of their interdisciplinary skills and knowledge, providing insights into their metacognition and epistemology (Lattuca et al., 2012); 2) Assessment of student assignments and presentations using published, peer-reviewed rubrics for interdisciplinarity; and 3) Analysis of student prompts generated with ChatGPT. The student prompts will be analysed using natural language processing techniques, as outlined in recent studies, to gain insights into students’ cognitive processes and modes of thinking (Lee et al., 2023). Through this triangulated approach, the overall aim of the study is to comprehensively and rigorously evaluate the cognitive processes underpinning interdisciplinary learning.
This lightning talk will specifically describe the insights gleaned from the analysis of students’ prompts so as to identify and evaluate possible learning barriers faced by students in the process of interdisciplinary learning.
Figure 1. Interdisciplinary approach to biotechnology innovation.
REFERENCES
Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62. psychology, 61, 101860. https://doi.org/10.1038/s41587-022-01253-x
Boms, O., Shi, Z., Mallipeddi, N., Chung, J. J., Marks, W. H., Whitehead, D. C., & Succi, M. D. (2022). Integrating innovation as a core objective in medical training. Nature Biotechnology, 40(3), 434-437.
Boix Mansilla, V., & Dawes Duraising, E. (2007). Targeted assessment of students’ interdisciplinary work: An empirically grounded framework proposed. Journal of Higher Education, 78(2), 215-237. https://doi.org/10.1080/00221546.2007.11780874
Ivanitskaya, L., Clark, D., Montgomery, G. et al. (2002). Interdisciplinary Learning: Process and Outcomes. Innovative Higher Education 27, 95–111. https://doi.org/10.1023/A:1021105309984
Lattuca, L. R., & Knight, D. B., & Bergom, I. M. (2012, June), Developing a Measure of Interdisciplinary Competence for Engineers. Paper presented at 2012 ASEE Annual Conference & Exposition, San Antonio, Texas. https://dx.doi.org/10.18260/1-2—21173
Lee, U., Han, A., Lee, J., Lee, E., Kim, J., Kim, H., & Lim, C. (2023). Prompt Aloud!: Incorporating image-generative AI into STEAM class with learning analytics using prompt data. Education and Information Technologies, 1-31. https://doi.org/10.1007/s10639-023-12150-4
Repko, A. F. (2007). Integrating interdisciplinarity: How the theories of common ground and cognitive interdisciplinarity are informing the debate on interdisciplinary integration. Issues in Integrative Studies, 25, 1-31. http://hdl.handle.net/10323/4501