Abstract

This paper explores how technology (AI-based dashboard) can be leveraged to better understand and support student mental wellbeing within and beyond the classroom. Participants will examine key predictors of wellbeing, assess the capabilities and limitations of AI tools, and collaboratively design data-informed interventions. Emphasis is placed on ethical and inclusive approaches that prioritise student care and responsible technology use in educational settings.

Session and activities

1. Introduction & Context 

 

Facilitator presents a brief overview of the study:

 

• Key mental health determinants (academic, behavioural, demographic, socioeconomic)

 

• Role of machine learning (Random Forest, 91% accuracy) in identifying at-risk students

 

• The implications of predictive analytics in higher education

 

2. Group Task: Data Exploration Scenario 

 

Instructions:

 

• Split participants into small groups (4–5 per group)

 

• Each group receives a dataset profile of 5 students (pre-defined attributes: background, attendance, accommodation type, digital engagement, etc.)

 

• Groups are asked to:
  1. 1. Identify which students might be at risk using visible trends

   

  1. 1. Discuss what additional (ethical) data might help improve prediction

   

  1. 1. Propose how educators or support teams could intervene

   

   

 

3. Tech in Practice: AI Demo Walkthrough

 

Facilitator walks through a visual simulation of a simplified Random Forest-based AI dashboard that flags risk levels. The group explores:

 

• What the model sees

 

• How it generates alerts

 

• How educators could act on its insights

 

4. Design Challenge: From Prediction to Support 

 

Groups reassemble to brainstorm one concrete intervention strategy (either in the classroom or pastoral setting) that could be supported by predictive insights. Each group shares:

 

• The proposed action

 

• Stakeholders involved

 

• Challenges and ethical considerations

 

5. Reflections & Wrap-up 

 

Open floor discussion:

 

• What excites or concerns you about using AI in student wellbeing?

 

• How could your institution begin using these tools responsibly?

Learning Outcomes:

 

• Understand key predictors of student mental health

 

• Engage with the potential and limitations of AI in education

 

• Co-create ideas for data-informed interventions

 

• Reflect on ethical, inclusive AI adoption in academic settings