Lighting Talks

Navigating Authentic Learning In The Age Of Generative AI

cdtlsd Lighting Talks , , , ,

Sharon LAU Pui Wan 

NUS-ISS 

sharon.lau@nus.edu.sg 

Lau, S. P. W. (2024). Navigating authentic learning in the age of generative AI [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-spwlau/

SUB-THEME

Opportunities from Generative AI 

KEYWORDS

Generative AI, Authentic Learning, Critical Thinking, Educational Strategies 

CATEGORY

Lightning Talk

EXTENDED ABSTRACT

In an era where generative AI is rapidly transforming educational landscapes, educators face both unprecedented opportunities and formidable challenges. As an adult educator in NUS, I propose to address the most pressing issue: how can we foster authentic learning experiences while leveraging the capabilities of generative AI? This lightning talk aims to provide a roadmap for educators to navigate this complex terrain, addressing the following aspects: 

Embrace AI as a co-educator 

Generative AI can serve as a dynamic partner in the educational process, providing personalised feedback, facilitating adaptive learning, and enabling interactive simulations. By integrating AI-driven tools, educators can create more engaging and tailored learning experiences. An example from my own practice involves using AI tools to simulate real-world business scenarios. Students interact with AI-generated market data, making strategic decisions that mirror professional challenges. This method not only deepens their understanding but also prepares them for the complexities of the professional world. The AI acts as a co-educator, providing immediate feedback and alternative perspectives, thereby enriching the learning experience. 

Cultivate critical thinking  

One of the core components of authentic learning is the development of critical thinking skills. AI can play a crucial role in this area by presenting students with complex, open-ended problems that require creative solutions. Research has shown that AI can aid in the development of higher-order thinking skills by challenging students to analyse, interpret, and synthesise information. For example, in my classes, I utilise AI to design challenging case studies that require students to engage deeply with the material. These case studies present AI-generated data that students must critically evaluate and interpret. This approach fosters a deeper level of engagement and helps students develop the critical thinking skills necessary to navigate an increasingly complex world. The use of AI in presenting diverse scenarios and data sets encourages students to consider multiple perspectives and develop well-rounded solutions. 

 

During this talk, I will draw from the latest research and my practical experiences to highlight key strategies and insights. I will share examples of real-world practice to illustrate my key messages. 

 

Putting Dispositions At The Heart Of Teaching Collaborative Inquiry-Based Discourse

cdtlsd Lighting Talks , , ,

Melvin NG Han Wei 

Department of Philosophy,
Faculty of Arts and Social Sciences (FASS), NUS 

mnhw@nus.edu.sg 

Ng, M. H. W. (2024). Putting dispositions at the heart of teaching collaborative inquiry-based discourse [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-mhwng/

SUB-THEME

Opportunities from Engaging Communities 

KEYWORDS

Thinking dispositions, inquiry, discursive pedagogy 

CATEGORY

Lightning Talk

EXTENDED ABSTRACT

Most accounts of intelligence are “ability-centric”, reducing intelligence to some measure of intellectual aptitude, e.g. IQ. However, intelligent behaviour “in the wild”—everyday situations where there are no carefully framed tests to indicate the intellectual task to be attempted—depends largely on thinking dispositions. That is, the main determinant of everyday intellectual activity is not ability, but the disposition to use the relevant ability. I argue that higher education should focus on developing dispositions, as opposed to merely training skills or imparting content knowledge. This is the only way to take seriously the role of higher education to give students the tools to apply advanced knowledge and skills across varied environments throughout their lives, in order to lead creative, productive careers. 

 

I explain Perkins et al. (1993)’s  “triadic” theory of thinking, which divides dispositions into three elements: inclination to use the relevant abilities, sensitivity to the occasion where one can use the relevant abilities, and the abilities themselves. I then use the triadic model to make suggestions on how one might put dispositions at the heart of teaching collaborative discursive-based inquiry, as exemplified by the community of inquiry (COI) described in Matthew Lipman’s (2003) Philosophy for Children (P4C) programme. As I see it, collaborative, discursive-based inquiry is more than just a pedagogy but embodies dispositions (understood triadically) necessary to participate in social discourse in a public sphere increasingly characterised by free contestation of ideas. As such, it embodies a vital set of dispositions for engaging in communities, however we define them, and performing the associated social intellectual activities “in the wild”, including listening empathetically, clarifying and extending ideas, and seeking common understandings and compromises. While tailored to K-12, the P4C model, not least how it envisions a COI, can certainly be adapted to higher education, and serves as a useful vehicle for developing critical discursive dispositions. I offer suggestions on how this can be done, focussing on the changes that have to be made to conceptualising lessons, role of the teacher and assessment.  

 

For many, putting on a “dispositions-lens” when teaching—as opposed to a “content-” or even “ability-lens”—will require a paradigm shift. Though I will focus on the teaching of collaborative inquiry-based discourse, it is my hope that the suggestions I offer will inspire dispositions-centred thinking in the teaching of other disciplines. 

REFERENCES​

Lipman, M. (2003). Thinking in Education (2nd ed.). Cambridge University Press. 

Perkins, D., Jay, E., & Tishman, S. (1993). Beyond abilities: A dispositional theory of thinking. Merrill-Palmer Quarterly-Journal Of Developmental Psychology, 39(1), 1–21. https://www.jstor.org/stable/23087298 

Perkins, D., Tishman, S., Ritchhart, R., Donis, K., & Andrade, A. (2000). Intelligence in the wild: A dispositional view of intellectual traits. Educational Psychology Review, 12(3), 269–293. https://doi.org/10.1023/A:1009031605464

Rethinking Assessment in the Age of GenAI

cdtlsd Lighting Talks , , ,

YANG Yi 

Department of Civil and Environmental Engineering,
College of Design & Engineering (CDE), NUS

yangyi@nus.edu.sg  

Yang, Y. (2024). Rethinking assessment in the age of GenAI [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-yangyi/

SUB-THEME

Opportunities from Generative AI 

KEYWORDS

Generative AI, assessment methodology, academic integrity 

CATEGORY

Lightning Talk

INTRODUCTION

The advent of generative AI (GenAI) has profoundly transformed various sectors, with education being no exception. As GenAI technology advances, it presents both opportunities and challenges to traditional educational practices. While GenAI can automate many teaching tasks such as generating content (such as Figures 1, 2, 3 generated by AI for the subsequent paragraphs) and preparing lesson plans, one primary concern among educators is upholding academic integrity, given that GenAI can complete many assessments with minimal human effort. To address these challenges and leverage the potential of GenAI, there is a need to rethink and redesign our assessment models. This paper explores the strategies to adapt our assessment approaches in alignment with GenAI to enhance education outcomes. 

BE CLEAR OF THE OBJECTIVE 

First and foremost, educators should define clear objectives for their assessments. The format of assessments should align with specific learning goals in the context of GenAI. For example, if the aim is to gauge students’ retention of basic concepts, an in-class test that forbids GenAI use might be the most suitable. In contrast, if the goal is to create an authentic environment and enhance students’ ability to search for and organise information, a take-home assignment will be suitable. As the availability of GenAI has already become part of the authentic learning environment, the use of GenAI should be permitted or even encouraged in such assessments. It is critical that when AI tools are accessible for the assessments, all questions and tasks should be tested with the latest version of GenAI to ensure the assessments meet the intended learning objectives. 

Figure 1. Be clear of the objectives of the assessment (image generated by Microsoft Copilot) 

EMPHASISE SOFT SKILLS 

Secondly, assessments should place greater emphasis on soft skills. In the age of GenAI, competencies such as communication skills and teamwork become even more vital, as many tasks that traditionally require hard skills can now be automated by AI. These soft skills truly differentiate humans from machines and are becoming increasingly valuable in the professional sphere. To cultivate these soft skills, educators may consider designing project-based assessments with greater scoring weightage on oral presentations, Q&A sessions, and role-playing activities [1]. These assessment models not only enhance the development of soft skills, but also eliminate concerns regarding AI-related academic integrity issues by evaluating real-time interactions. 

Figure 2. Emphasise soft skills in assessment (image generated by Microsoft Copilot) 

INTEGRATE AI INTO ASSESSMENT MODELS 

Thirdly, integrating AI into assessments can foster innovative evaluation models. Instead of viewing AI merely as a challenge to academic integrity, educators and institutions can harness its capabilities to enrich and enhance learning experiences. For instance, an assessment could involve students reviewing and critiquing answers generated by GenAI. In an Urban Planning and Design course at the University of Hong Kong, students are tasked to: i) generate an essay using GenAI tools, ii) critically evaluate the AI-generated content, and iii) compose their own essay based on their critique [2]. This approach not only deepens students’ understanding of the subject but also helps them recognise the strengths and limitations of AI tools. By engaging with AI-generated content, students also develop critical thinking and analytical skills as they learn to evaluate and refine the information provided.  

Figure 3. Integrate genAI in assessment design (image generated by Microsoft Copilot)   

CONCLUDING REMARKS 

Educators should swiftly adapt to the evolving landscape of AI technology. By aligning assessments with clear learning objectives, emphasising the development of soft skills, and leveraging AI capabilities in assessment design, educators can create a more effective and relevant educational experience. This proactive approach ensures that students are not only knowledgeable in their subjects but also well-prepared to navigate and excel in a world increasingly shaped by AI.

REFERENCES​

Sharma, R. C., & Bozkurt, A. (2024). Transforming education with generative AI: Prompt engineering and synthetic content creation. IGI Global. 

Chan, C. K. Y., & Colloton, T. (2024). Generative AI in higher education: The ChatGPT effect (1st ed.). Taylor & Francis. 

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

Leveraging Multi-Agent Generative AI for Next-Generation Education and Career Development

cdtlsd Lighting Talks , , , , ,

WANG Hongtai* and YEO Wee Kiang 

Department of Information Systems and Analytics,
School of Computing (SOC), NUS

*e1132287@nus.edu.sg

Wang, H., & Yeo, W. K. (2024). Leveraging multi-agent generative AI for next-generation education and career development [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-hwang-wkyeo/

SUB-THEME

Opportunities from Generative AI 

KEYWORDS

Educational Support, Career Advisory, Generative AI, Multi-Agent System, Large Language Model 

CATEGORY

Lightning Talk

INTRODUCTION

In today’s complex education and career planning landscape, students face challenges in navigating vast resources and making informed decisions. Traditional career advisory services often lack the necessary personalisation. This project aims to leverage AI technologies, particularly large language models (LLMs) and multi-agent systems, to develop a novel solution that provides tailored guidance and support. 

The system’s functionality is divided into two core functionalities: Tutor and Career Advisory. Together, these features create a cohesive system that guides students through both academic and career development with personalised solutions. 

KEY CAPABILITIES 

Tutor Section 

  • Tutorial Teaching: This function introduces subject matter concepts progressively, from simple to complex, with clear examples for better understanding. 
  • Question-and-Answer: This function allows users to ask questions about the course materials and receive contextually relevant answers. 
  • Providing Review Questions: This function generates questions based on provided materials, testing students’ mastery of concepts through customised review questions. 

 

Career Advisory Section 

  • Generate Interview Questions. Generates tailored interview questions based on job descriptions by analysing required skills and knowledge. 
  • Analyse Shortcomings. Compare the student’s resume and the job description to highlight skill and knowledge gaps. 
  • Generate Cover Letters. Automatically examine the job description and user’s resume, extract the most relevant resume highlights, and generate a cover letter emphasizing the user’s advantages for the role. 
  • Assist Users in Gaining Essential Skills. Creates technology tutorials tailored to job descriptions, providing links to online courses and materials to enhance learning. 

METHODOLOGY 

Architecture 

The architecture of this project is designed as a dialogue system based on a generative AI model. The core component is a LLM agent that acts as a traffic controller and interacts with the user. Based on the user’s input prompt, this agent will discern whether the user’s queries are related to Tutor or Career Advisory and thereafter provide appropriate responses accordingly. 

If the user’s request is straightforward, the LLM agent handles it directly. For more complex requirements, the agent collects detailed information about the user’s needs and activates specialised agent groups (Cheng et al., 2024) designed to address specific issues. An agent group consists of multiple LLM agents created using Autogen (Wu et al., 2023) that is responsible for one specific task. These specialised agent groups include: 

  1. Education Agent Groups: Responsible for tasks such as coaching users with course materials, answering their questions, and providing review questions. 
  2. Career Agent Groups: Responsible for generating interview questions, analysing the user’s shortcomings, generating cover letters, and providing learning resources. 

Figure 1. Diagram of our system with LLM agent and role-based groups.

KEY FINDINGS 

Interface Design 

In the Tutor section, users engage directly with agent tutors through a system that allows seamless switching between instructional content and question-and-answer sessions. 

Figure 2. Our AI platform assists users in learning Operations Management through dialogue. 

 

The Career section offers unrestricted communication with AI-powered LLM agents, providing essential career advisory. This includes generating personalised tables that identify skill gaps relative to job requirements and assisting in crafting tailored cover letters to improve job applications. 

Figure 3. User interface for generating a comparison table between job requirements and applicant qualifications. 

SIGNIFICANCE OF THE PROJECT 

  • Enhanced Personalisation: Tailored learning experiences with progressive subject matter introduction, customized Q&A support, and personalised review questions. Career guidance includes tailored interview questions, skill gap analysis, and personalized cover letters. 
  • Improved Accessibility: 24/7 support with easy access to relevant online courses and materials. 
  • Efficiency: Streamlined assistance through specialized agent groups, providing quick and precise help. Identifies skill gaps and offers targeted learning recommendations. 
  • Career Preparation: Generates mock interview questions and skill gap analyses for better preparation. Automatically creates personalised cover letters and offers resume advice. 

REFERENCES​

Cheng, Y., Zhang, C., Zhang, Z., Meng, X., Hong, S., Li, W., Wang, Z., Wang, Z., Yin, F., Zhao, J., & He, X. (2024). Exploring large language model-based intelligent agents: Definitions, methods, and prospects.  ArXiv. abs/2401.03428. 

Wu, Q., Bansal, G., Zhang, J., Wu, Y., Li, B., Zhu, E., Jiang, L., Zhang, X., Zhang, S., Liu, J., Awadallah, A.H., White, R.W., Burger, D., & Wang, C. (2023). AutoGen: Enabling next-gen LLM applications via multi-agent conversation. ArXiv. abs/2308.08155v2.

Mentoring for Everyone’s Well Being

cdtlsd Lighting Talks , ,

Deborah Ann CHOO* and Julie GOUIN

Centre for English Language Communication (CELC)   

*elcchoo@nus.edu.sg

Choo, D. A., & Gouin, J. (2024). Mentoring for everyone's well being [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-dachoo-jgouin/

SUB-THEME

Opportunities from Wellbeing 

KEYWORDS

Mentoring, wellbeing 

CATEGORY

Lightning Talk

EXTENDED ABSTRACT

Mentoring new colleagues is an important part of integrating new staff into the university setting. This presentation is part of a case study through document analysis which evolved from a mentoring relationship. Together we hypothesised that a fruitful mentoring relationship leads to mentee and mentor wellbeing, consequently promoting student wellbeing. We define wellbeing broadly to encompass both the hedonic and eudaimonic forms (Zuo et al., 2017). 

 

Our experience aligns with the literature that good mentoring leads to professional development (Gilles & Wilson, 2004; Hudson, 2013) and retention of new staff (Gardiner et al., 2007; Kanaskie, 2006; Laband & Lentz, 1995). Our workplace handbook, which references Lipscomb and An’s (2010) recommendation to create a structured mentoring relationship also delineates the roles and responsibilities of the mentor and mentee (Lee et al., 2017). We found that throughout our ongoing relationship, we each assumed many of the roles from the handbook and other literature, but mentor as “role model” (Kram, 1983, Levesque et al., 2005, Olian et al., 1988, Shen & Kram, 2011) led to an ethic of care (Noddings, 2012) and thus, we contend, to student wellbeing. Furthermore, Riva et al., (2020) found that teachers’ care for students at the university level correlates with student wellbeing. 

 

Our mentoring relationship was initiated by our management, based on our assignment to teach the same course. Mentoring activities included discussions about the course objectives and materials, addressing students’ questions, and responding to mentee’s questions. The mentor also anticipated items that might need clarification or elaboration and oriented the mentee to the course, the department, the broader opportunities, and the physical environment. A Microsoft (MS) Teams group, created by the mentor, facilitated sharing and reflection on classroom activities among the mentor, mentee, and two additional colleagues. The mentee drove the relationship by seeking clarification, observing the mentor’s class, reflecting on pedagogy, and implementing useful ideas for student learning and the promotion of wellbeing. 

 

In our study, we quantified the data by creating tables using three of Zuo et al.’s six dimensions to uncover evidence of wellbeing in the following documents: student feedback, the course coordinator’s review of the mentee, the mentee’s review of the mentor and the MS Teams forum. The three dimensions were then separated according to hedonic and eudaimonic wellbeing. Additionally, we produced tables using four selected categories of care ethics (Noddings 2012) to analyse selected documents. We also created a table to demonstrate the connections between mentor, mentee and student care and wellbeing, analysing all documents in addition to mentor-mentee communications. 

 

Our analysis showed that the mentor’s modeling with her own students and suggestions for care positively impacted mentor, mentee and student wellbeing. For instance, the mentor displayed care by asking students about their sleep and encouraging open communication about stress. She also suggested that the mentee survey her students, which led to a positive change in the mentee’s teaching style as reported through the student feedback exercise. Furthermore, like the mentor’s engagement with student wellbeing, the student feedback exercise reflected the mentee’s use of this approach as a strength and thus, the mentee’s wellbeing. Further analysis of this student feedback showed a distribution of wellbeing across the three dimensions (Zuo et al., 2017). Additionally, mentor wellbeing was evidenced in the mentee’s observation and MS Teams forum, using the same method for analysis. Overall, there was a predominance of eudaimonic wellbeing for mentor, mentee, and students across all documents. These findings are significant because they support the importance of effective onboarding of new staff members. Well-executed mentoring relationships increase the likelihood of positively impacting the mentee, the mentor, and the students. 

REFERENCES

Gardiner, M., Tiggemann, M., Kearns, H., & Marshall, K. (2007). Show me the money! An empirical analysis of mentoring outcomes for women in academia. Higher Education Research & Development, 26(4), 425–442. 10.1080/07294360701658633 

Gilles, C., & Wilson, J. (2004). Receiving as well as giving: mentors’ perceptions of their professional development in one teacher induction program. Mentoring & Tutoring: Partnership in Learning, 12(1), 87–106. https://doi.org/10.1080/1361126042000183020 

Hudson, P. (2013). Mentoring as professional development: ‘growth for both’ mentor and mentee. Professional Development in Education, 39(5), 771–783. https://doi.org/10.1080/19415257.2012.749415 

Kanaskie, M. L. (2006). Mentoring—A staff retention tool. Critical Care Nursing Quarterly, 29(3), 248-252. https://dx.doi.org/10.1097/00002727-200607000-00010 

Kram, K. E. (1983). Phases of the mentor relationship. Academy of Management Journal, 26(4), 608-625. https://doi.org/10.5465/255910 

Laband, D. N., & Lentz, B. F. (1995). Workplace mentoring in the legal profession. Southern Economic Journal, 61(3), 783–802. https://doi.org/10.2307/1060998 

Lee, G, Tang, J., & Tan, S. H., (2017). Educator’s Development Programme (EDP) Mentoring Handbook. Centre for English Language Communication, National University of Singapore. 

Levesque, L. L., O’Neill, R. M., Nelson, T., & Dumas, C. (2005). Sex differences in the perceived importance of mentoring functions. Career Development International, 10(6/7), 429-443. https://dx.doi.org/10.1108/13620430510620539 

Lipscomb, R., & An, S., (2010). Mentoring 101: Building a mentoring relationship. Journal of American Dietetic Association. https://doi.org/10.1016/j.jand.2013.02.010 

Noddings, N. (2012). The caring relation in teaching. Oxford Review of Education, 38(6), 771– 781. https://dx.doi.org/10.1080/03054985.2012.745047 

Olian, J. D., Carroll, S. J., Giannantonio, C. M., & Feren, D. B. (1988). What do protégés look for in a mentor? Results of three experimental studies. Journal of Vocational Behavior, 33(1), 15-37. https://dx.doi.org/10.1016/0001-8791(88)90031-0 

Riva, E., Freeman, R., Schrock, L., Jelicic, V., Özer, C.-T., & Caleb, R. (2020). Student wellbeing in the teaching and learning environment: A study exploring student and staff perspectives. Higher Education Studies, 10(4), 103. https://doi.org/10.5539/hes.v10n4p103 

Shen, Y., & Kram, K. E. (2011). Expatriates’ developmental networks: Network diversity, base and support functions. Career Development International, 16(6), 528-552. https://doi.org/10.1108/13620431111178317 

Student Feedback Report. (2024). National University of Singapore. 

Zuo, S., Wang, S., Wang, F., & Shi, X. (2017). The behavioural paths to wellbeing: An exploratory study to distinguish between hedonic and eudaimonic wellbeing from an activity perspective. Journal of Pacific Rim Psychology, 11(10). https://dx.doi.org/10.1017/prp.2017.1

 

The Role Of Intra-Personal Competencies In Sustaining Success In Continuous Learning

cdtlsd Lighting Talks , , , , ,

Alfred CHAN* and Marcus WEE 

Lifelong Education and Training,
School of Continuing and Lifelong Education (SCALE)

*alfredch@nus.edu.sg 

Wee, M. & Chan, A. (2024). The role of intra-personal competencies in sustaining success in continuous learning [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-achan-mwee/

SUB-THEME

Opportunities from Wellbeing 

KEYWORDS

Awareness, motivation, intra-personal, insights, growth 

CATEGORY

Lightning Talk

EXTENDED ABSTRACT

This research investigates the critical role of intra-personal skills—self-awareness, self-regulation, and self-motivation—in supporting continuous learning within today’s rapidly evolving technological and job market environments. The study hypothesises that these skills significantly enhance adaptability, goal-setting, and persistence in learning contexts. 

 

Employing a mixed-methods approach, the research collects quantitative data through surveys of participants in continuous learning programs. These surveys measure intra-personal capabilities and their impact on learning outcomes. Additionally, qualitative insights from interviews and discussions provide a deeper understanding of how these skills are utilised in real-world learning scenarios. 

 

The findings reveal that individuals with strong intra-personal skills are better equipped to adapt to new information, achieve learning goals, and maintain motivation despite challenges. The study emphasises the importance of mastering these competencies to effectively navigate dynamic environments and proposes practical strategies for cultivating them among learners in Continuing Education and Training (CET) programmes.

 

Ultimately, the enhancement of intra-personal skills empowers individuals to engage in lifelong learning, fostering both personal and professional growth. 

 

The Usage of Generative Artificial Intelligence (AI) Avatars in Psychiatric Medical Education

cdtlsd Lighting Talks , , ,

Charlene GOH Xing Le1, Eugene WANG1, Judy C. G. SNG2,*

1Yong Loo Lin School of Medicine (YLLSOM), NUS
2Department of Pharmacology, YLLSOM, NUS 

*phcsngj@nus.edu.sg

Sng, J. C. G., Goh, C. X. L., & Wang, E. (2024). The usage of generative artificial intelligence (AI) avatars for psychiatric medical education [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-jcgsng-et-al/

SUB-THEME

Opportunities from Generative AI 

KEYWORDS

Opportunities from Generative AI, video avatars, patient simulation, medical education

CATEGORY

Lightning Talk

BACKGROUND

The integration of generative artificial intelligence (AI) into our daily lives has significantly increased in recent years, with numerous healthcare institutions leveraging AI and medical technology to enhance diagnostic and treatment accuracy. However, its utilisation in medical education remains limited. This project aims to bridge this gap by promoting the incorporation of AI into medical education. This is done by supplementing existing resources with AI-generated videos of simulated patients. Currently, the range of available avatars online is limited and not entirely relevant to the context of Singapore. By providing virtual scenarios featuring patients with psychiatric medical conditions, we aim to develop essential skills in medical students, such as empathy and sensitivity, within a controlled environment. This approach seeks to enhance students’ clinical skills and decision-making, ensuring they are well-prepared for real-world patient interactions as practitioners. 

METHODS

AI technology was employed at multiple stages of building the video avatars. Initially, a doctor- patient conversation is generated using ChatGPT-4, after inputting a prompt that corresponds to a specific psychiatric patient profile. The patient profile includes the positive and negative signs presented by the patient, their socio-economic background, past medical history, and the suggested treatment plan. Medical experts in the psychiatric field meticulously crafted the simulated patient’s information to ensure an accurate representation of the condition. Two main symptoms were chosen: low mood (including adjustment disorder, dysthymia, and major depressive disorder) and chest tightness (not amounting to chest pain, including adjustment disorder, panic disorder, and somatic symptom disorder). After fine-tuning the script to ensure the scenario was localised and culturally accurate to Singapore’s context, the D-iD software was utilised to generate an AI video of a patient, emulating sentiments and synchronising speech with facial expressions. 

 

The video is then shown to students, whose objective is to accurately diagnose the patient. The patient avatar video aims to increase interactivity with students in a classroom setting, enhancing their diagnostic skills and understanding of psychiatric conditions. 

RESULTS

Preliminary feedback from medical professionals suggests its great potential to become a staple resource for healthcare students. In the coming months, we plan to pilot this with a small group of healthcare students to gather feedback and suggestions to refine the ensemble of avatars we currently have. The data collection will include metrics such as diagnostic accuracy and effectiveness in improving the students’ knowledge. Further studies will be conducted to determine the usage and effectiveness in nurturing the holistic development of medical students, encompassing both academic and psychosocial skills. 

CONCLUSION

As AI integrates more seamlessly into the medical curriculum, it is crucial to cultivate an engaging, interactive, and safe space for students, bridging the gap between theoretical knowledge and clinical practice. We believe that incorporating AI as early as possible in medical education is vital for developing holistic practitioners for a sustainable future, ultimately improving patient care in healthcare institutions. 

 

Infusing Contextual Elements With Generative AI Tools To Reinforce Learning For Students

cdtlsd Lighting Talks , , , ,

TAN Chun Liang

Department of Architecture,
College of Design and Engineering (CDE), NUS

tcl@nus.edu.sg 

Tan, C. L. (2024). Infusing contextual elements with generative AI tools to reinforce learning for students [Lightning talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-cltan/

SUB-THEME

Opportunities from Generative AI 

KEYWORDS

Urban Greening, Site context, Video assignment, Peer review 

CATEGORY

Lightning Talk

EXTENDED ABSTRACT

The recent rise of AI content generators has had significant impact on both learning and teaching in educational institutions. Although the university encourages the responsible use of AI content-generation tools, a point of concern is on the authenticity of student report submissions. How do we ensure that usage of such platforms can help us augment the learning experience and not become a tool for students to conveniently churn out AI-generated reports at the eleventh hour to submit as their own?  

 

This is an important question to address for the course LA5303 “Urban Greening: Technologies and Techniques”, where students are taught ways to utilise urban greenery to improve the environment. Without a real understanding of the course content, students may not learn about the proper ways of urban greening and fall back to more superficial and cosmetic treatments of the landscape, leading to greenwashing. 

 

Drawing on the Significant Learning pedagogical framework (Fink, 2013), I try to tap on the Human Dimension, in learning about oneself in others to reinforce learning. I explored peer learning and the theory of Distributed Cognition (Hutchins, 2020) to acquire knowledge through an individual’s social and physical environment. In this manner, cognitive resources can be shared socially, and students can achieve more as a group than with just individual effort.  

 

Strategy 1: Experiential learning, peer learning, and industry talks 

Singapore is adorned with many examples of urban greenery projects, ranging from ground-level parks to sky-rise greenery projects. Since this course is about urban greenery, I began to introduce more activities to encourage more experiential and peer learning: where students go for site visits, assess real projects in Singapore and learn from each other instead of having me give them second- or third-hand information via lectures.

 

Figure 1. Learning journey to SproutHub @ Henderson  

 

Learning journeys to prominent urban greening spaces such as SproutHub@Henderson (Figure 1) and talks by industry partners (Figure 2) were organised to let students gain first-hand experience of the components of urban greenery covered in this course.  

Figure 2. Talks by Mr Christopher Leow, prominent urban farmer (Top row) and Representatives from Elmich Pte Ltd, sharing green wall and green roof products (Bottom row) 

 

In addition, students were tasked to visit urban greenery projects of their choosing and to create bite-sized (1-minute) videos documenting their learning (Figure 3). The videos were subsequently uploaded onto an online bulletin board (Miro) for others to review and comment on (Figure 4). Students were given the opportunity to provide comments on the videos produced by their peers. This directly increased their learning of urban greenery projects by 50 (total number of students in the course), as knowledge is now crowdsourced and contextual.  

 

Figure 3. Screenshots of a video done by a student, documenting urban greenery learning  

 

Figure 4. Comments from peers on the Miro board 

 

Strategy 2: Pre-emptive strike to nullify the impact of A.I. generated content  

Instead of warning students not to use AI-generated content, I insisted that it was the first thing they did for their assignment. Students had to append their prompts and AI-generated results in their report and show how they built on the AI content to come up with their final report. The AI-generated content thus became another layer of educational scaffolding for the students. More importantly, students were instructed to include specific examples of how to improve on urban greening using examples of videos done by their peers from Strategy 1. In this case, it became less likely for students to cheat with AI content generators such as ChatGPT because the examples are unique. 

REFERENCES​

Fink, L. D. (2013). Creating significant learning experiences: An integrated approach to designing college courses. John Wiley & Sons.

Hutchins, E. (2020). The distributed cognition perspective on human interaction. In Roots of human sociality (pp. 375-398). Routledge. 

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

cdtlsd Lighting Talks , , , , ,

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

Viewing Message: 1 of 1.
 Warning

Blog.nus accounts will move to SSO login soon. Once implemented, only current NUS staff and students will be able to log in to Blog.nus. Public blogs remain readable to non-logged in users. (More information.)