Smart Calendar: Integrating AI for Student Mental Health and Wellbeing

1, *Akshay Narayan, 1LI Jiayao, 1Bimlesh Wadhwa, 2Alex MITCHELL, 3Eric KERR, and 2Weiyu ZHANG

1Department of Computer Science, School of Computing (SOC)
2Department of Communications and New Media, Faculty of Arts and Social Sciences (FASS)
3Tembusu College and Asia Research Institute (ARI)

*dcsaksh@nus.edu.sg

Narayan, A., Li, J., Wadhwa, B., Mitchell, A., Kerr, E., & Zhang, W. (2024). Smart Calendar: Integrating AI for student mental health and wellbeing [Poster presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singaporehttps://blog.nus.edu.sg/hecs/hecs2024-anarayan-et-al/

SUB-THEME

Opportunities from Wellbeing 

KEYWORDS

Smart calendar, AI, Time management, Rest and recovery, Wellbeing 

CATEGORY

Paper Presentation

 

INTRODUCTION

University students face high cognitive loads and often feel they lack time for academic activities (Kyndt et al., 2014), increasing their stress levels (Kausar, 2010). Time management is a suggested coping strategy (Macan et al., 1990) that enhances academic performance and reduces anxiety (Razali et al., 2018). However, many students struggle with time management due to juggling academic, extracurricular, and personal commitments (Xu et al., 2014). 

 

Good time management helps students analyze tasks, plan effectively, and understand task priorities (Nonis et al., 2006; Sauvé et al., 2018). It leads to academic success and enhances life quality beyond university (Wang et al., 2011). However, students need support to develop these skills (Van der Meer et al., 2010). 

 

THE CORE CHALLENGE 

Despite its importance, studies haven’t focused on effective intervention mechanisms for time management or directly considered students’ mental wellbeing. Research often mentions stress reduction as a side effect but doesn’t address it directly. With AI advancements, we can now provide targeted interventions. AI can offer personalised scheduling and proactive reminders, including breaks and relaxation periods, to support both time management and mental wellness. 

 

OUR PROPOSED STRATEGY 

Our proposal results from discussions within a Technology for Social Good learning community. Recognising student mental wellness concerns at NUS, we explored technological solutions. We suggest a two-pronged approach using AI with a “smart calendar.” First, automate task planning and scheduling to reduce cognitive load and include intervention mechanisms. Second, incorporate mental wellness by automatically scheduling “recovery time” in the task schedule. 

 

Addressing mental health and wellbeing 

It is assumed that better time management improves mental health by reducing stress from unfinished tasks. However, most studies overlook the need for recovery and relaxation after demanding tasks. Research shows regular micro-breaks and sufficient sleep enhance productivity (Kim et al., 2018; Kühnel et al., 2017). 

 

We propose incorporating sleep schedules and explicit micro-breaks, proportional to the duration and the demand of the cognitive task, automatically in task scheduling. For example, a two-hour core-course lecture should be followed by a thirty-minute recovery break in the schedule. 

 

Automating time management 

We believe AI can enhance task scheduling by automating it using the following inputs: 

  • System input: Fixed academic schedules from university sources like timetables and LMS. 
  • User inputs: Personal schedules defining non-academic and extracurricular activities. 

 

We solve the task scheduling problem algorithmically, treating it as a constraint satisfaction and resource optimization issue. Given the fixed, limited available time, we aim to optimise its utilisation. Tasks and micro-breaks are modeled as constraints for the schedule. Additionally, the system can learn user behavior and preferences to refine the schedule. 

 

The smart calendar also nudges students (Caraban et al., 2019) to record task completion and follow scheduled micro-breaks, providing targeted interventions for mental wellbeing.

 

THE PATH FORWARD 

Currently, we have formulated a Master’s thesis project for developing an AI-driven smart calendar that addresses the intertwined challenges of time management and mental wellness for university students. Under this project, we are prototyping a calendar application based on the discussions arising out of the learning community focused on Technology for Social Good. The calendar aims to provide personalised, proactive support, helping students manage their busy schedules with ease and confidence. Going forward, we intend to perform a user study to measure the effectiveness of such a calendar application among university students. We believe as educational institutions prioritise mental wellness, adopting AI calendaring solutions could significantly foster a healthier, more productive student community.  

 

REFERENCES

Caraban, A., Karapanos, E., Gonçalves, D., & Campos, P. (2019). 23 ways to nudge: A review of technology-mediated nudging in human-computer interaction. In Proceedings of the 2019 CHI conference on human factors in computing systems,  

Kausar, R. (2010). Perceived stress, academic workloads and use of coping strategies by university students. Journal of Behavioural Sciences, 20(1). https://pu.edu.pk/images/journal/doap/PDF-FILES/3rd-article-Vol-20-No-1-2010.pdf 

Kim, S., Park, Y., & Headrick, L. (2018). Daily micro-breaks and job performance: General work engagement as a cross-level moderator. Journal of Applied Psychology, 103(7), 772. https://psycnet.apa.org/doi/10.1037/apl0000308

Kühnel, J., Zacher, H., De Bloom, J., & Bledow, R. (2017). Take a break! Benefits of sleep and short breaks for daily work engagement. European Journal of Work and Organizational Psychology, 26(4), 481-491. https://doi.org/10.1080/1359432X.2016.1269750

Kyndt, E., Berghmans, I., Dochy, F., & Bulckens, L. (2014). ‘Time is not enough.’ Workload in higher education: a student perspective. Higher Education Research & Development, 33(4), 684-698. https://doi.org/10.1080/07294360.2013.863839  

Macan, T. H., Shahani, C., Dipboye, R. L., & Phillips, A. P. (1990). College students’ time management: Correlations with academic performance and stress. Journal of Educational Psychology, 82(4), 760. https://psycnet.apa.org/doi/10.1037/0022-0663.82.4.760  

Nonis, S. A., Philhours, M. J., & Hudson, G. I. (2006). Where Does the Time Go? A Diary Approach to Business and Marketing Students’ Time Use. Journal of Marketing Education, 28(2), 121-134. https://doi.org/10.1177/0273475306288400  

Razali, S., Rusiman, M., Gan, W., & Arbin, N. (2018). The impact of time management on students’ academic achievement. Journal of Physics: Conference Series. https://iopscience.iop.org/article/10.1088/1742-6596/995/1/012042  

Sauvé, L., Fortin, A., Viger, C., & Landry, F. (2018). Ineffective learning strategies: a significant barrier to post-secondary perseverance. Journal of Further and Higher Education, 42(2), 205-222. https://doi.org/10.1080/0309877X.2016.1224329  

Van der Meer, J., Jansen, E., & Torenbeek, M. (2010). ‘It’s almost a mindset that teachers need to change’: first‐year students’ need to be inducted into time management. Studies in Higher Education, 35(7), 777-791. https://doi.org/10.1080/03075070903383211  

Wang, W.-C., Kao, C.-H., Huan, T.-C., & Wu, C.-C. (2011). Free time management contributes to better quality of life: A study of undergraduate students in Taiwan. Journal of Happiness Studies, 12, 561-573. https://doi.org/10.1007/s10902-010-9217-7

Xu, J., Yuan, R., Xu, B., & Xu, M. (2014). Modeling students’ time management in math homework. Learning and Individual Differences, 34, 33-42. https://doi.org/10.1016/j.lindif.2014.05.011 

Leveraging AI Tools for Engagement and Higher Learning Outcomes: An Exploratory Study

Sadaf Ansari

Ridge View Residential College (RVRC)

sadaf@nus.edu.sg

 

Ansari, S. (2024). Leveraging AI tools for engagement and higher learning outcomes: An exploratory study [Paper presentation]. In Higher Education Conference in Singapore (HECS) 2024, 3 December, National University of Singapore. https://blog.nus.edu.sg/hecs/hecs2024-s-ansari/

SUB-THEME

Opportunities from Generative AI

 

KEYWORDS

Higher-order learning, AI, interdisciplinarity

 

CATEGORY

Paper Presentation 

 

EXTENDED ABSTRACT

The release of open-access Generative artificial intelligence (GenAI) has made its use in higher education inevitable (Mollick, 2024). Educators’ initial concerns, which centred on unreliability and plagiarism, have since been replaced by universities embracing the technology, with a focus on the positive potential of AI in learning (NUS AI COP, 2023; CTI Cornell, 2023). Emerging literature on the use of open-access GenAI indicates the possibilities for enhancing student learning and transfer of knowledge when paired with specific inquiry-related skills such as critical thinking and ideation (Gregersen, 2023; Mollick, 2024). This is rapidly shifting the pedagogic focus in using GenAI from ‘what works’ to the possibilities offered by ‘what if?” (Ross, 2023). Set within such a context, this exploratory case study evaluated the impact of integrating GenAI on student engagement and higher-order learning outcomes for an interdisciplinary course.

 

BACKGROUND

Recent studies indicate that the use of GenAI can positively impact transfer; once students have gained foundational knowledge, GenAI can provide a time-efficient mode to test multiple ideas for complex problems, beyond traditional modes (Eapen, 2023; Mollick, 2022). This can enhance student engagement by simplifying the process of knowledge application, and build their proficiency in cognitively-demanding higher learning outcomes (Kirschner et al., 2022)

 

PROJECT AIMS AND SCOPE

The study was conducted with 32 students reading an interdisciplinary NUS General Education course—RVSS1000 “Exploring Sense of Place”—taught in small classes of 14-18 students using a seminar-style format. The class composition was multidisciplinary; it included students in their first and second year of undergraduate studies from six different NUS faculties. The course instructor’s observations from teaching the course across several semesters highlighted that students often lacked engagement and struggled with the cognitive demands of ideation—a higher learning outcome—while working on the final project. This was compounded by the inherent challenge of integrating creative ideas within complex spatial parameters of a real site and communicating these ideas effectively to stakeholders. End-of-semester time constraints further exacerbated these challenges for students. The course instructor evaluated the impact on these three interconnected problems encountered during ideation, by enabling students to automate the process of visualisation using GenAI – Microsoft CoPilot.

 

METHOD

The impact of integrating GenAI was evaluated using three types of data – a) the course instructor’s observations of student engagement with ideating project solutions using GenAI, b) analysis of students’ visualisations for the project using GenAI, and c) a student perception survey about using GenAI. An exploratory case study method was adopted for data collection and evaluation, due to the fast-evolving context-specific nature of GenAI capabilities.

 

KEY FINDINGS

Overall, findings from the study demonstrated the amelioration of the three interconnected problems encountered by students. Firstly, GenAI helped integrate students’ ideas within the complex spatial parameters of any real-world site by drawing on its vast database of site-specific photographs. Secondly, GenAI produced realistic three-dimensional visualisations that enhanced effective communication of students’ creative ideas to external partners and stakeholders. Lastly, automating the visualisations freed students’ time to focus instead on the higher cognitive demand of generating creative ideas for the project and iterative refinements.

 

The course instructor’s observational data indicated an increase in positive student engagement during project discussions and consultations; compared to previous cohorts, students experimented more with complex creative ideas, provided meaningful peer feedback, and undertook effective improvements of their projects to incorporate peer and instructor feedback. During the formal presentation, the use of GenAI also enabled students to fully explain their creative ideas and engage stakeholders in discussions about implementation, a limitation faced by previous cohorts.

 

Students’ use of GenAI for the project demonstrated their ability to undertake the cognitively-demanding task of synthesising creative ideas anchored by robust interdisciplinary analysis. This was evidenced in the use of GenAI to scale the spaces correctly and connect site-specific details about materials, landscape, biodiversity, stakeholder priorities, and function. In some exemplary projects, students used GenAI to further elaborate spatial features and cited relevant literature justifying their ideas for implementation.

 

The student perception survey conducted at the end of the course included questions about the impact of using GenAI on the three challenges identified by previous cohorts, using a Likert scale. Students strongly agreed about the interconnected benefits of using GenAI for enabling the cognitively demanding task of ideation by automating the process of visualisation and iterative refinement, with scores for the six questions ranging from 73%-79%.

 

While it is difficult to establish causation, data from NUS student feedback also showed minor improvements in students’ expected grades and course difficulty levels with the use of GenAI. Similar improvements were noted for ‘teacher’s ability to enhance interest in the subject’ and ‘overall effectiveness’. The addition of a new AI-related question in the NUS Student Feedback Survey in AY2024/25 offers an opportunity to clarify this further.

 

Despite the learning affordances of using GenAI identified by students, survey data about their recommendation for its use by future cohorts showed mixed results, with only 56% supporting it. 24% decided against using GenAI due to a perceived steep learning curve. 21% were unsure due to their own struggles with using GenAI, as they discovered that prompt engineering was not an intuitive skill. Hence, the study concluded that decisions about the continued integrative use of GenAI in this course (and perhaps others) must also address such valid concerns and avoid creating new digital disparities in student learning (NUS CTLT, 2024).

 

REFERENCES

CU Committee Report: Generative Artificial Intelligence for Education and Pedagogy | Center for Teaching Innovation. (n.d.). Retrieved December 14, 2023, from https://teaching.cornell.edu/generative-artificial-intelligence/cu-committee-report-generative-artificial-intelligence-education

Eapen, T. T., Finkenstadt, D. J., Folk, J., & Venkataswamy, L. (2023, July 1). How generative AI can augment human creativity. Harvard Business Review. https://hbr.org/2023/07/how-generative-ai-can-augment-human-creativity.

Gregersen, H., & Bianzino, N. M. (2023, May 26). AI can help you ask better questions-and solve bigger problems. Harvard Business Review. https://hbr.org/2023/05/ai-can-help-you-ask-better-questions-and-solve-bigger-problems

Jensen, T., Dede, C., Tsiwah, F., & Thompson, K. (2023, July 27). Who Does the Thinking: The Role of Generative AI in Higher Education [Video]. YouTube. International Association of Universities. Retrieved December 12, 2023, from https://www.youtube.com/watch?v=gE_GKsdTPAs.

Kirschner, P. A., Hendrick, C., & Heal, J. (2022). How Teaching Happens: Seminal Works in Teaching and Teacher Effectiveness and What They Mean in Practice. Routledge.

Mollick, E. (2024). Co-intelligence: Living and working with AI. Portfolio/Penguin.

Mollick, E.R., and Mollick, L., (December 13, 2022). New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments. Available at SSRN: https://ssrn.com/abstract=4300783 or http://dx.doi.org/10.2139/ssrn.4300783

NUS CTLT (2024, August 7). Policy for Use of AI in Teaching and Learning. Retrieved August 25, 2024, from https://ctlt.nus.edu.sg/wp-content/uploads/2024/08/Policy-for-Use-of-AI-in-Teaching-and-Learning

Ross, J. (2023). Digital Futures for Learning: Speculative Methods and Pedagogies. Routledge.

 

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