ChatGPT

Harnessing GPT to Develop a Personalised eLearning Web Application for Java Programming

cdtlsd Lighting Talks , , , ,

LIU Fan*, Esther TAN Meng Yoke, and CHIA Yuen Kwan 

NUS-ISS 

*isslf@nus.edu.sg  

Liu, F., Tan, E. M. Y., & Chia, Y. K. (2024). Harnessing GPT to develop a personalised elearning web application for Java programming [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-lfan-et-al/

SUB-THEME

Opportunities from Generative AI 

KEYWORDS

Generative AI, Personalised eLearning, ChatGPT, Java Programming 

CATEGORY

Lightning Talk

BACKGROUND

E–learning systems are gaining popularity due to their extensive scalability, flexibility, self-paced capability as well as easy accessibility. Artificial Intelligence (AI) has been used in e-Learning systems (Murtaza et al., 2022; Sayed et. al., 2023) to make personalised learning recommendations to learners. Specifically, Generative Pre-trained Transformers (GPT) such as ChatGPT (OpenAI, 2023) has transformed education by offering intelligent assistance, customised content, personalised learning materials to cater to the individual learner’s needs (Rasul, 2023; Jin & Kim, 2023; Gharbi et al., 2024). AI-based eLearning can significantly enhance learning efficiency through personalised content delivery to learners (Bozkurt, 2021; Chen et al., 2023). However, these AI-based personalised eLearning techniques have not been effectively integrated to provide diverse and dynamic eLearning content for learners based on their personalities and learning preferences. 

PURPOSE

Propose an approach to provide dynamic and personalised content for learners through integrated Generative AI techniques. We developed an eLearning web application that connects to ChatGPT API, which will automatically generate dynamic course contents/exercises/quizzes based on the learners’ personality such as level of language proficiency and personal learning preferences for Java foundations. 

RESEARCH QUESTIONS

  1. How to automatically generate dynamic and personalised Java foundational content for each learner on the fly?
  2. How to automatically generate dynamic and personalised coding exercise questions for each learner on the fly?
  3. How to provide a more efficient learning experience for each learner based on the learner’s personality and learning preferences? 

METHOD

Design and develop an eLearning web application that seamlessly integrates the following technologies:

  1. Automatically assesses learner’s learning preferences using the Felder-Silverman learning style model (FSLSM) (Felder & Silverman, 2012). The FSLSM categorises learners into specific dimensions based on their preferred ways of acquiring and processing information. This model is used in our eLearning application to customise the course delivery format to suit the learners’ learning preferences.
  2. Harness the GPT model to generate dynamic course contents
  3. Provide diverse and personalised eLearning content based on learners’ personality and learning preferences.

 

The general architecture of the eLearning application consists of three modules: web browser client, eLearning server, and Microservices (ChatGPT server). The web browser client enables learners to interact with the eLearning application. The eLearning server delivers the core functionality of the system which includes the generation of dynamic topics, personalised exercises, and quizzes while the Microservice represents the external ChatGPT server.  

KEY FINDINGS 

This study explores how ChatGPT with FSLSM can be harnessed to create a customised Java programming course and provide learners with dynamic and personalised course materials. This method promotes a customised learning journey catering to the different learner’s needs in terms of their levels of Java proficiency and learning preferences.  

FUTURE WORK

The future work of the proposed eLearning application is summarised as follows: (1) using ChatGPT model to generate comprehensive course content including advanced topics of Java, (2) extend this research work to generate dynamic and personalised course content for other object-oriented programming languages as C++, C#, and Python, (3) introduce foundational topics, exercises and quizzes with support of different learning methods, such as hands-on workshops, auto-grading and learning feedback and report., (4) extend the application of the personalised eLearning application to include other academic disciplines. This future work should consider the challenges of the use of ChatGPT in education with regards to the accuracy and reliability of content.

REFERENCES​

Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldán, A. E., & Rodríguez, M. E. (2021). Artificial intelligence and reflections from educational landscape: A review of AI Studies in half a century. Sustainability, 13(2), 800. https://doi.org/10.3390/su13020800

Chen, E., Huang, R., Chen, H. S., Tseng, Y. H., & Li, L. Y. (2023, June). GPTutor: a ChatGPT-powered programming tool for code explanation. In International Conference on Artificial Intelligence in Education (pp. 321-327). Springer Nature Switzerland. 

Felder, R., & Silverman, L. (2012). Index of learning styles questionnaire. North Carolina State University. Retrieved from http://www.engr.ncsu.edu/learn ingst yles/ilswe b.html. 

Gharbi, M., Taib Mohtadi, M., & Fal Merkazi, A. (2024). Revolutionizing Moocs with fine-tuned Chatgpt: Personalized Learning At Scale. International Journal of Computing and Digital Systems, 16(1), 1-11. 

Jin, J., & Kim, M. (2023). GPT-Empowered Personalized eLearning System for Programming Languages. Applied Sciences, 13(23), 12773. https://doi.org/10.3390/app132312773

Murtaza, M., Ahmed, Y., Shamsi, J. A., Sherwani, F., & Usman, M. (2022). AI-based personalized e-learning systems: Issues, challenges, and solutions. IEEE Access, 10, 81323–81342. https://dx.doi.org/10.1109/ACCESS.2022.3193938

OpenAI (2023). ChatGPT: Optimizing Language Models for Dialogue. https://openai.com/index/chatgpt/

Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W. J., & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching, 6(1), 41-56. http://dx.doi.org/10.37074/jalt.2023.6.1.29

Sayed, W. S., Noeman, A. M., Abdellatif, A., Abdelrazek, M., Badawy, M. G., Hamed, A., & El-Tantawy, S. (2023). AI-based adaptive personalized content presentation and exercises navigation for an effective and engaging E-learning platform. Multimedia Tools and Applications, 82(3), 3303-3333. https://doi.org/10.1007/s11042-022-13076-8

Harnessing Generative AI As A Personalised Tutor: Enhancing Interdisciplinary Learning Outcomes For Biotechnology Graduate Students

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Xin Xiang LIM 

Department of Biological Sciences,
Faculty of Science, NUS 

xinxiang@nus.edu.sg 

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

Leveraging Chatgpt For Analysing Student Reflections In A Design Thinking Course

cdtlsd Paper Presentations , , , , ,

Qian HUANG1,*, Ameek Kaur2, Thijs WILLEMS1

1Lee Kuan Yew Centre for Innovative Cities, Singapore University of Technology and Design (SUTD)
2NUS Business School

*qian_huang@sutd.edu.sg

Huang, Q., Kaur, A., & Willems, T. (2024). Leveraging ChatGPT for analysing student reflections in a design thinking course [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/qhuang-et-al/

SUB-THEME

Opportunities from Generative AI 

KEYWORDS

Generative AI, ChatGPT, large-scale reflection, qualitative analysis, design education 

CATEGORY

Paper Presentation 

 

EXTENDED ABSTRACT

Generative Artificial Intelligence (Gen-AI) is increasingly being integrated into teaching and research methodologies, particularly since the advent of ChatGPT (Albdrani & Al-shargabi, 2023; Hwang & Chen, 2023). As educators navigate this evolving landscape, it becomes crucial to understand how to effectively and critically utilise Gen-AI tools in academic settings. This study explores the application of ChatGPT in analysing student reflections in a design thinking course at a university in Singapore. The course involved 550 first-year students across 11 cohorts, each student required to write four reflections over a semester. The significant volume of reflections presented a unique opportunity to deploy ChatGPT-4.0 for large-scale qualitative analysis. 

 

Initially, researchers manually analysed the reflections of 50 students from one class to establish a benchmark. These manual analyses were then compared to ChatGPT’s results to verify the reliability of the AI-driven approach. Upon confirming ChatGPT’s reliability, the tool was employed to analyse reflections from the entire cohort through the semester (550 students X four phases). The analysis focused on two primary objectives: first, to assess the impact of pedagogical interventions on students’ Affect, Behavior, and Cognition (ABC); and second, to understand how students applied these interventions and the frequency of their application. 

 

The study aimed to uncover how specific pedagogical interventions influenced students’ emotional responses, behavioural changes, and cognitive developments by using ChatGPT. For instance, it was observed that interventions such as confirmation bias were frequently applied by students during site visits to explore problems from multiple perspectives. This detailed analysis provided insights into the effectiveness of various teaching strategies and highlighted areas for potential improvement. 

 

Key findings from the study revealed several noteworthy trends. Firstly, some interventions, including case studies and activities, did not significantly impact students’ affective responses to the ideas emphasised in these interventions. This suggests that educators may need to refine these interventions to better support students emotionally. Secondly, the analysis highlighted variations in the delivery and emphasis of interventions across different cohorts, attributable to individual teaching styles of different instructors. ChatGPT’s analysis provided a nuanced understanding of how these differences influenced student outcomes. 

 

By leveraging ChatGPT, the research team was able to conduct a comprehensive analysis of a large dataset, providing valuable insights that might not have been feasible through manual analysis alone. The findings underscore the potential of Gen-AI tools in educational research, particularly in scaling qualitative analyses and uncovering patterns that inform pedagogical practices. 

 

In summary, this study demonstrates the utility of ChatGPT in analysing student reflections to gauge the impact of pedagogical interventions on students’ action, emotion, and cognition. The application of Gen-AI in this context not only facilitated the processing of a large volume of qualitative data but also offered educators deeper insights into how classroom interventions can be optimised to achieve desired educational outcomes. This method represents a significant advancement in educational research, providing a scalable and reliable approach to understanding and enhancing student learning experiences. 

 

This study contributes to the growing body of literature on the use of AI in education and offers practical implications for educators seeking to integrate Gen-AI tools into their teaching practices. Future research could expand on these findings by exploring the application of ChatGPT in different educational contexts and with diverse student populations to further validate and refine this approach. 

 

REFERENCES

Albdrani, R., & Al-shargabi, A. (2023). Investigating the effectiveness of ChatGPT for providing personalized learning experience: A case atudy. International Journal of Advanced Computer Science and Applications. https://doi.org/10.14569/ijacsa.2023.01411122.  

Hwang, G. J. & Chen., N. S. (2023). Exploring the potential of generative artificial intelligence in education: Applications, challenges, and future research directions. Educational Technology & Society, 26(2). https://doi.org/10.30191/ETS.202304_26(2).0014 

Exploring The Effects of An Artificial Intelligence (AI) Chatbot on Learning and Motivation Among Pharmacy Students

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Lik-Wei WONG1*, Amanda Huee-Ping WONG1, Valerie Ying Hui TAN2, Embang Johann Emilio GONZALES2 and Shing Chuan HOOI1

1Department of Physiology, Yong Loo Lin School of Medicine (YLLSOM), National University of Singapore (NUS)
2Alice Lee Centre for Nursing Studies, YLLSOM, NUS

*phswlw@nus.edu.sg

Wong, L.-W., Wong, A. H.-P., Tan, V. Y. H.,  Gonzales, E. J. E., & Hooi, S.C. (2024). Exploring The Effects of An Artificial Intelligence (AI) Chatbot on Learning and Motivation Among Pharmacy Students [Lightning Talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-wong-et-al

 

SUB-THEME

Opportunities from Generative AI

KEYWORDS

AI chatbot, ChatGPT, learning, motivation, undergraduate

CATEGORY

Lightning Talk

INTRODUCTION

The rapid advancements in Artificial Intelligence (AI) technologies have prompted us to re-evaluate the future of our education. Although AI has great potential to enhance teaching and learning, its role in pedagogy and instruction has not been fully studied. Motivation has been shown to influence students’ learning approaches, their engagement level, their persistence in accomplishing goals, and their thinking processes (Chiu, 2022). Ryan and Deci (2017; 2020) propose Self-Determination Theory (SDT) suggesting that autonomous motivation is the preferred type of motivation for learning as it can lead to greater engagement and persistence. A recent study has found that university students who engaged with AI chatbots had greater intrinsic motivation than those who did not. These findings imply that students may feel more comfortable and engaged when interacting with chatbots, potentially leading to increased expression of ideas (Yin et al., 2021) and higher levels of motivation (Fryer et al., 2019).

RATIONALE OF STUDY

As AI technology continues to advance, its impact on the education of medical and health professionals will be significant. While some argue that it may have negative implications for students’ learning, educators should consider incorporating AI technology into their teaching methods to enhance students’ learning experiences. This study aims to investigate the potential of AI chatbots as a pedagogical tool for enhancing learning and motivation among pharmacy students.

METHODS

Participants in this study were second-year undergraduate pharmacy students enrolled in the PR2153 course on the Cardiovascular System during Semester 1 of AY2023/24. For the physiology components of the course, students were provided with various educational resources, such as eBooks, online lecture videos, and quizzes for self-directed learning, before attending in-person classroom discussions. Students were encouraged to submit questions via a designated Question & Answer (Q&A) link and to use ChatGPT to find answers to their questions. The teachers would then evaluate ChatGPT’s responses and provide further clarifications, where necessary. Additionally, ChatGPT was incorporated into a case-based group discussion. To evaluate the AI chatbot’s impact on motivation, we used the established SDT and Intrinsic Motivation Inventory (IMI) in a post-course anonymous survey questionnaire. The survey included two open-ended questions about the AI chatbot’s strengths and limitations. Additionally, focus group discussions were conducted and analysed thematically to determine AI chatbot’s effects on learning and motivation.

KEY FINDINGS

60.2% (50/83) of the students participated in and completed the survey, using ChatGPT for their study of cardiovascular physiology. Overall, needs satisfaction (3.59 ± 0.81) was significantly higher (p<0.001) in students who used ChatGPT for their studies compared to those who did not (needs satisfaction: 2.98 ± 0.76). Students who used ChatGPT demonstrated significantly higher levels (p<0.05) of all three components—autonomy, competence, and relatedness. Additionally, students who used ChatGPT showed higher interest (p<0.001) and found value (p<0.001) in using the AI chatbot. These results indicate that AI chatbots promote students’ motivation. In general, students found ChatGPT to be a useful tool for generating fast, easy-to-understand answers and provoking ideas. These benefits, in turn, facilitated their learning and the development of metacognitive skills. However, students were also aware of its limitations, particularly regarding accuracy, credibility, and generalized answers.

SIGNIFICANCE OF THE STUDY

This study found that students who engaged with the AI chatbot exhibited greater intrinsic motivation, potentially leading to increased expression of ideas and promoted thinking, thereby enhancing learning and boosting overall motivation. Therefore, the use of AI chatbots should be encouraged to supplement learning by incorporating them alongside traditional teaching resources.

REFERENCES

Chiu, T. K. (2021). Applying the self-determination theory (SDT) to explain student engagement in online learning during the COVID-19 pandemic. Journal of Research on Technology in Education, 54(1), S14-S30. https://doi.org/10.1080/15391523.2021.1891998

Fryer, L. K., Nakao, K., & Thompson, A. (2019). Chatbot learning partners: Connecting learning experiences, interest and competence. Computers in Human Behavior, 93, 279-289. https://doi.org/10.1016/j.chb.2018.12.023

Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press.

Ryan, R. M., & Deci, E. L. (2020). Intrinsic and extrinsic motivation from a self-determination theory perspective: Definitions, theory, practices, and future directions. Contemporary educational Psychology, 61. https://doi.org/10.1016/j.cedpsych.2020.101860

Yin, J., Goh, T.-T., Yang, B., & Xiaobin, Y. (2020). Conversation technology with micro-learning: The impact of chatbot-based learning on students’ learning motivation and performance. Journal of Educational Computing Research, 59(1), 154-177. https://doi.org/10.1177/07356331209520

Creating Videos with ChatGPT and AI Voiceover for Higher Cognitive Engagement

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WU Jinlu1* and Uday Satyamohan Athreya2

1Department of Biological Sciences, Faculty of Science
2Centre for Teaching, Learning, and Technology (CTLT)

*dbswjl@nus.edu.sg

Wu, J., & Athreya, U. S. (2024). Creating Videos with ChatGPT and AI Voiceover for Higher Cognitive Engagement [Lightning Talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-wu-and-uday

SUB-THEME

Opportunities from Generative AI

KEYWORDS

ChatGPT, Scriptwriting, AI voiceover, Videos, Higher Cognitive Engagement

CATEGORY

Lightning Talk

EXTENDED ABSTRACT

Educational videos have become integral to modern pedagogical strategies, enhancing student engagement, managing cognitive loads, and deepening concept mastery. However, producing high-quality educational videos traditionally requires sophisticated equipment, such as advanced microphones, quiet environments, and professional editing software. Non-native speakers and individuals who are camera-shy often find the process particularly challenging. Furthermore, updating videos to reflect the latest findings and current trends can be time-consuming.

 

Advancements in Artificial Intelligence (AI) have revolutionised the creation of educational content, making it more accessible and effective. By leveraging tools like ChatGPT for scriptwriting and AI voiceovers for narration, educators can significantly enhance cognitive engagement in educational videos. These tools allow the generation of scripts tailored to various pedagogical focuses, such as triggering curiosity, engaging students in scientific inquiry, or fostering critical thinking. Additionally, updating slides and scripts with dynamic, current academic content or creating scenario-based learning and assessments can be done efficiently.

 

In this talk, I will share my experiences using ChatGPT and AI voiceover technology on our internal platform, VoiCeIT (https://voiceit.nus.edu.sg), to create educational videos. I will cover the processes involved, including scriptwriting, slide importing, and video exporting. Furthermore, I will discuss how these videos have been integrated into a blended learning environment and present student feedback on their use.

A5 - Fig 1

Figure 1. A graphic summarising the four key steps for video production using ChatGPT and AI voiceover

 

There are several benefits to using VoiCeIT for video production. Firstly, the app is free to use and provides access to technical support when needed. Secondly, it eliminates the need for dedicated recording spaces, equipment, and additional manpower. Lastly, it is time-efficient for producing new videos or updating existing ones, significantly increasing productivity.

 

My videos were developed for teaching Molecular Genetics in a blended learning format. They were made available on Canvas, allowing students to watch them before or after lectures. Although there were no direct assignments or assessments linked to these videos, data showed an increase in both the number of students watching and the duration of views for the new AI-voiceover videos compared to traditional videos recorded via Camtasia.

 

Additionally, I explored using ChatGPT for scriptwriting with different audiences and learning objectives in mind. While the core academic content remained consistent, I prompted ChatGPT to write scripts aimed at “sparking curiosity,” “fostering scientific inquiry skills,” and “promoting critical thinking.” These prompts align with the pedagogical goals of initiating interest, supporting skill development, and encouraging deep thinking.

Table 1
An example showing three slides on an ageing topic used to generate three video scripts for different pedagogical purposes

Slides Prompts
Given the scientific content below, please write video scripts aimed at
sparking curiosity fostering inquiry skills promoting critical thinking
Slide 1:

Telomere and telomerase

 Have you ever wondered…? Intrigued? Let’s dive deeper! Have you ever wondered…?
How does this process
impact…? Let’s explore the science behind it!”		 Have you considered why …? What does this mean for cellular longevity and aging? Let’s delve into the mechanics and implications…”
Slide 2:

Cell type-specific telomere dynamics

 Did you know…? Curious about how this impacts aging? Keep watching! Let’s form a hypothesis: Do different cell types experience telomere shortening at different rates?
…How can we investigate
…? By examining…and comparing the data, we can draw…		 Why do telomeres shorten at different rates in various cell types?… Analyzing these differences, what can we infer
…?
Slide 3:

Telomere attrition, cellular senescence, dysfunction, and ageing

 How do short telomeres affect our
health? …Want to know how to keep your cells healthier for longer?
Let’s explore!		 What role do short telomeres play in diseases? Let’s hypothesize…To investigate, we can collect data on… By examining the correlation between…, we can conclude … How does telomere shortening contribute to cardiovascular disease?… Analyzing these influences, how can we better understand…? What critical steps can we take to mitigate these effects?

 

The potential broad application of this approach extends beyond biology to various fields and educational levels. By reducing the technical and production barriers, educators can focus more on content quality and pedagogical impact. AI-driven video production not only democratises content creation but also ensures that educational materials can be rapidly updated and tailored to meet diverse learning needs.

 

In conclusion, integrating ChatGPT and AI voiceover technology in educational video production can enhance cognitive engagement, making learning more interactive and effective. This approach has significant pedagogical implications, offering a scalable solution for creating dynamic, current, and pedagogically focused educational content.

 

Bridging the Gap: Comparing Student Beliefs and Existing Empirical Data on ChatGPT’s Job Market Impact

cdtlsd Paper Presentations , , , ,

Jingcheng FU
Residential College 4 (RC4), NUS

jingcheng.fu@nus.edu.sg

Fu, J. (2024). Bridging the gap: Comparing student beliefs and existing empirical data on ChatGPT’s job market impact [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-jingchengfu/

SUB-THEME

Opportunities from Generative AI

KEYWORDS

ChatGPT, student beliefs, job characteristics, labour market trends

CATEGORY

Paper Presentation 

 

INTRODUCTION

The integration of AI technologies, particularly language models like ChatGPT, is poised to transform various job sectors and higher education (Dempere et al., 2023). This study explores student beliefs about ChatGPT’s impact on the labour market. Specifically, students are asked to predict about the association between different skill requirements and other job characteristics and the exposure to ChatGPT. The beliefs are elicited in a class survey after which the students are informed of the latest empirical findings. Understanding these beliefs is crucial for educators and career advisors to guide students effectively in a rapidly evolving job market.

 

LITERATURE REVIEW

Eloundou et al. (2023) developed an “exposure index” to measure the extent to which different jobs are affected by ChatGPT. Specifically, based on a detailed description of the work activities and tasks for each job, the researchers use some rubrics to determine the proportion of tasks that ChatGPT is expected to make at least 50% faster. This index is computed for 1,016 occupations in the US labour market. The occupation dataset also provides quantitative measures of job characteristics, including the importance of different skills. Many of those characteristics are significantly correlated with the exposure index.

 

RESEARCH QUESTIONS

  1. What are students’ perceptions of the relationship between a job’s requirements of different types of skills and the exposure to ChatGPT?
  2. How do these student beliefs compare with the existing empirical findings?

 

METHODOLOGY

To investigate these questions, a survey was administered to 34 first-year NUS students who took the course UTC1702G “Thinking in Systems – Markets and Inequality” in April 2023. The class was 55% female, and the faculty composition was 32% NUS Business School, 16% Faculty of Arts and Social Sciences (FASS), 14% Faculty of Science (FOS), 27% School of Computing (SOC), 9% College of Design and Engineering (CDE), and 2% from the Multi-disciplinary Programme. Students were given a brief explanation of the study design of Eloundou et al. (2023), together with the definitions of the skills, before they answered the multiple-choice questions (MCQs) to make guesses on the findings. I first asked students which skills are positively associated with substitutability, followed by which skills are negatively associated; the answers were coded as “Negative”, “Positive”, and “Neutral” for each skill (2% of the answers for a particular skill were inconsistent and not included in the analysis).


KEY FINDINGS

Figure 1 summarises the findings of Eloundou et al. (2023) and the distribution of student beliefs. Of the 11 skills tested, all are significantly associated with exposure except for speaking. For the three skills that are most strongly positively associated with exposure—reading, writing, and programming—the vast majority of student beliefs were accurate. For the other skills, however, their guesses departed from the paper’s findings. For the two skills that are most strongly negatively associated with exposure, only around 50% of students correctly predicted the association, and around 30% of the students believed the opposite. For the other two process-related skills, learning strategies and monitoring, which have a small negative association with exposure, the guesses were split, with more than 40% being incorrect positive guesses. Only 20-30% of students correctly saw the positive association for active listening, and the negative association for mathematics and science.

HECS2024-a86-Fig1Figure 1. Beliefs about associations between ChatGPT exposure and different skill requirements compared to empirical findings.

 

SIGNIFICANCE OF THE STUDY

The findings indicate a significant disparity between student beliefs and the empirical data from the exposure index study. Many students hold incorrect assumptions about which skills and job characteristics are most vulnerable to AI substitution. This gap underscores the need for educational interventions to align student perceptions with actual labour market trends. By identifying these misconceptions, educators can develop targeted strategies to enhance career guidance and support, ensuring students have accurate information about AI’s effects on job characteristics and skills. This is essential for preparing them to navigate future career paths effectively. Addressing these misconceptions is crucial for students’ future success, providing a foundation for further research and practical applications in higher education and career planning..

 

REFERENCES

Dempere, J., Modugu, K., Hesham, A., & Ramasamy, L. K. (2023). The impact of ChatGPT on higher education. In Frontiers in Educatio, 8, 1206936.  https://doi.org/10.3389/feduc.2023.1206936

Eloundou, T., Manning, S., Mishkin, P., & Rock, D. (2023). GPTs are GPTs: An early look at the labor market impact potential of large language models. https://doi.org/10.48550/arXiv.2303.10130

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