Enhancing Educational Outcomes in Quality and Productivity Management Through ChatGPT Integration

Alexander LIN*, Anqi SHI, and Stephen En Rong TAY 

Department of the Built Environment, College of Design and Engineering (CDE), NUS

*bdgal@nus.edu.sg 

Lin, A., Shi, A., & Tay, S. E. R. (2024). Enhancing educational outcomes in quality and productivity management through ChatGPT integration [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-alin-et-al/

SUB-THEME

Opportunities from Generative AI

KEYWORDS

Artificial Intelligence, Educational Technology, Interactive Learning, Management Education, Student Engagement 

CATEGORY

Paper Presentation

 

INTRODUCTION 

This study examines the impact of integrating Generative AI tools, specifically ChatGPT, into the course PF2203 “Quality and Productivity Management (QPM)”. It focuses on fostering Collaborative Constructivism and Authentic Application of QPM concepts. Collaborative Constructivism enhances understanding and retention (Brooks & Brooks, 1999; Piaget, 2013), while Authentic Application translates theoretical knowledge into practical scenarios (Rahmawati et al., 2021; Weeks et al., 2019). These methodologies significantly boost critical thinking, as evidenced by studies like Sandu et al. (2024) and Rahman and Watanobe (2023), which suggest that student interactions with ChatGPT promote such outcomes. By integrating ChatGPT, the study anticipates improved student engagement and critical thinking in practical QPM applications. The research questions are:   

  1. Does ChatGPT integration enhance learning, as shown by student work? 
  2. What are student perceptions of ChatGPT integration? 

 

METHODOLOGY

This study employs methods similar to those used by Sandu et al. (2024) and Rahman and Watanobe (2023), enhancing student engagement and critical thinking through interactions with ChatGPT in PF2203. Groups of 2 to 3 students utilised ChatGPT to prepare their group projects, which included gathering information, synthesising content, and discussing a QPM-related topic through oral presentations. The presentations also had a segment on students’ sharing of their ChatGPT usage, followed by a class discussion. The effectiveness of ChatGPT was assessed through both qualitative and quantitative student feedback, as well as students’ presentation scores, which were evaluated based on the marking criteria in Table 1. The study received ethical approval from the Learning and Analytics Committee in April 2024. 

Table 1 
Marking scheme for project presentation
 

HECS2024-a42-Fig1

 

DISCUSSION AND RESULTS

Through tutorial discussions, the lecturer promoted creative use of ChatGPT, including integrating theoretical knowledge with practical cases. As illustrated in Figures 1 and 2, which depict examples from students’ Q&A with ChatGPT and presentation slides, respectively, students learned this approach and applied lean principles at different phases of a construction project in their presentation. Concurrently, the students were aware of the potential errors in the output generated by ChatGPT. For instance, Table 2 reveals their capability to discern when the output does not align closely with the required information or context. Consequently, they could exclude irrelevant data, showcasing their critical thinking skills in assessing and utilising the information from ChatGPT effectively. Overall, student presentation scores, which were evaluated based on criteria emphasising concept integration and practical understanding (see Table 1), show significant improvement in the Academic Year 2023/24 Semester 2 with the use of ChatGPT, compared to the previous year without ChatGPT (see Table 3). Averaged scores rose from 13.25 to 14.75 for the 1st Presentation and from 14.25 to 15 for the 2nd Presentation, showing improved capability of integration of QPM knowledge, aligning with the course’s learning objectives. However, as there were only four groups, statistical analysis was not feasible due to the small sample size. 

 

As indicated in Table 4, feedback collected using a 5-point Likert scale demonstrates a consensus that ChatGPT enhances knowledge integration (Question 1) and motivates learning (Question 2). Responses to Questions 3 and 4 generally confirm improvements in scenario-based learning and critical thinking through ChatGPT. The qualitative feedback from students, presented in Table 5, shows that integrating ChatGPT bolsters students’ critical thinking in applying managerial concepts in real-world contexts—effectively bridging the identified research gap. 

Figure 1. Student’s prompt for ChatGPT 

 

Figure 2. Student’s work for second presentation

 

Table 2
Students’ remark on
ChatGPT’s output. The first column records the prompts used and the responses from ChatGPT, and the second column contains their remarks on how they utilised ChatGPT’s output, as stipulated by a template provided by the lecturer, along with their evaluations of the output’s relevance and accuracy.
 

 

Table 3 
Averaged presentation scores (For each semester, number of student groups = 4)

 

Table 4 
End-of-
course survey results indicating the average response based on a five-point Likert scale with 1 (Strongly Disagree) and 5 (Strongly Agree) (n= 10).
 

HECS2024-a42-Fig4

 

Table 5 
Qualitative student feedback
 
 

HECS2024-a42-Fig5

 

CONCLUSION

Integrating ChatGPT into the QPM course has led to measurable improvements in both student engagement and academic performance. This suggests a scalable model that is sufficiently applicable for broader adoption across various educational disciplines. Future research will focus on optimizing AI tool integration within these contexts.

 

REFERENCES

Brooks, J. G., & Brooks, M. G. (1999). In search of understanding: The case for constructivist classrooms. Ascd. 

Piaget, J. (2013). The construction of reality in the child. Routledge. 

Rahman, M. M., & Watanobe, Y. (2023). ChatGPT for education and research: Opportunities, threats, and strategies. Applied Sciences, 13(9), 5783. https://doi.org/10.3390/app13095783

Rahmawati, Y., Taylor, E., Taylor, P. C., & Koul, R. (2021). Student empowerment in a constructivist values learning environment for a healthy and sustainable world. Learning Environments Research, 24, 451-468. https://doi.org/10.1007/s10984-020-09336-9

Sandu, R., Gide, E., & Elkhodr, M. (2024). The role and impact of ChatGPT in educational practices: insights from an Australian higher education case study. Discover Education, 3(1), 71. https://doi.org/10.1007/s44217-024-00126-6

Weeks, K. W., Coben, D., O’neill, D., Jones, A., Weeks, A., Brown, M., & Pontin, D. (2019). Developing and integrating nursing competence through authentic technology-enhanced clinical simulation education: Pedagogies for reconceptualising the theory-practice gap. Nurse Education in Practice, 37, 29-38. https://doi.org/10.1016/j.nepr.2019.04.010  

Enhancing Content Creation with Gen AI Tools for Teaching and Learning

Prakash S/O Perumal Haridas*, Teong Jin TAN and Muhamad Faizal Bin Ibrahim

Centre for Teaching, Learning, and Technology

*citpph@nus.edu.sg

Prakash P. H., Tan, T. J., & Muhamad Faizal Ibrahim. (2024). Enhancing Content Creation with Gen AI Tools for Teaching and Learning [Lightning Talk]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-prakash-et-al

 

SUB-THEME

Opportunities from Generative AI

 

KEYWORDS

AI-Assisted Content Creation, Educational Technology, Multimedia, Video Production, Video Post-Production

 

CATEGORY

Lightning Talk

EXTENDED ABSTRACT

The massive rise and spread of Generative Artificial Intelligence (Gen AI) technologies has ushered in a new era; one of opportunity, creativity, and efficiency. This lightning talk will cover how educators can leverage this era of Gen AI to enhance their content creation pipeline and easily create a variety of multimedia collaterals for teaching and learning. The talk will focus on three key AI-assisted applications for educational content creation. These are Image Generation, Video Generation, and Voice Generation.

 

The talk will give an overview of Image Generation and what it involves, including showing examples of AI-assisted high-quality image creations with examples of the prompts used to generate them. These will cover a broad range of subject matter across various disciplines and in different visual styles to show just how flexible this can be for educators. With how much Gen AI technology has continued to improve, educators simply input their desired text in a natural language and easily receive multiple output versions from Text to Image generators (Costin, 2024). This can be useful for creating visuals, illustrations, and diagrams that can help enhance the learner’s understanding and engagement. These can also work well for different types of learners, making it easier for more people to learn and understand new things.

 

The next section covering Video Generation will be split into two parts: Image-to-video and text-to-video. Related video-generated example snippets will be shown in the same format and across various subject matter like what was earlier shown for Image Generation, together with the prompts that were used to generate them. The image-to-video generation will be useful for educators in helping to give life to static photographs and visuals. In an animated form, these moving visuals will help to further complement the educational content and make it more visually appealing for the learner. For text-to-video, it is more of a wildcard in that generated results can be unpredictable (Weatherbed, 2024) but the potential for this technology (Dolak, 2024) and where it can go is worth a mention (Vynck et al., 2024). Some video snippets of what this technology can do once it reaches another level of maturity and photorealism (Germanidis, 2024) will be shown here.

 

Voice Generation involves a process of AI-assisted voice cloning, allowing educators to generate and replicate voices that mimic human voice patterns and intonations (Ashworth, 2023). Some audio snippet examples will be shown to give educators a better idea of the strength of this technology. This will be helpful for educators to easily create personalised content for learners. With their generated synthetic voices, educators only need to record their real voice once and then leverage AI to create as many types of audio content as possible (Lee, 2023). This will come as a real time saver and will help educators to be more efficient.

 

To wrap up, the talk will highlight how Gen AI comes into play in the overall video production pipeline. An example flowchart will be shown to illustrate this process and the areas where Gen AI can create an impact (Sparrow, 2024). Educators no longer need to worry too much about technicalities and can instead focus their energy on pedagogical innovation. By the end of the talk, educators will hopefully walk away more confident in their knowledge of what a content creation pipeline entails and how they can enhance their own content by harnessing Gen AI tools to produce engaging, adaptable, and inclusive learning experiences.

 

REFERENCES

Ashworth, B. (2023). AI can clone your favorite podcast host’s voice. Wired. https://www.wired.com/story/ai-podcasts-podcastle-revoice-descript/

Costin, A. (2024). Adobe advances creative ideation with the new firefly image 3 model. Adobe Blog. https://blog.adobe.com/en/publish/2024/04/23/adobe-advances-creative-ideation-with-new-firefly-image-3-model

Dolak, K. (2024). Toys “R” US debuts first video ad using Sora, OpenAI’s text-to-video tool. The Hollywood Reporter. https://www.hollywoodreporter.com/business/digital/toys-r-us-ad-sora-openai-video-tool-reaction-1235932993/

Germanidis, A. (2024). Introducing gen-3 alpha: A new frontier for video generation. Runway. https://runwayml.com/blog/introducing-gen-3-alpha/

Lee, T. B. (2023). I cloned my voice with A.I and my mother couldn’t tell the difference. Slate Magazine. https://slate.com/technology/2023/04/descript-playht-ai-voice-copy.html

Sparrow, M. (2024). Adobe announces new AI tools for Premiere Pro. Forbes. https://www.forbes.com/sites/marksparrow/2024/04/15/adobe-announces-new-ai-tools-for-premiere-pro/

Vynck, G. D., Elker, J., & Remmel, T. (2024). The future of AI video is here, super weird flaws and all. The Washington Post. https://www.washingtonpost.com/technology/interactive/2024/ai-video-sora-openai-flaws/

Weatherbed, J. (2024). Adobe Premiere Pro Is getting generative AI video tools – and hopefully OpenAI’s Sora. The Verge. https://www.theverge.com/2024/4/15/24130804/adobe-premiere-pro-firefly-video-generative-ai-openai-sora

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