AI, Data Analytics and Strategic Leadership

This workshop explores how contemporary artificial intelligence can support school leadership, institutional learning and evidence-informed decision-making. Participants will examine state-of-the-art developments - including multimodal AI, deep research, natural-language data analysis, configurable assistants and agentic systems - and consider how these capabilities may reshape teaching, assessment, professional roles, school operations and educational leadership.

Particular attention will be given to how schools can capture and interpret meaningful 'organic data' generated through everyday learning and organisational activity. This may include student work, assessment responses, discussions, surveys, observations, staff and student feedback, participation patterns and operational information. The aim is to obtain richer insights from evidence schools already generate, without creating excessive reporting demands, unnecessary data collection or surveillance.

Objectives and Contents

• Explore current and emerging AI capabilities relevant to school leadership.
• Consider major trends and possible changes in curriculum, assessment, teaching, professional practice and school organisation.
• Identify where AI can improve analysis, planning, communication and institutional learning.
• Map the quantitative and qualitative data naturally generated within a school.
• Design learning interactions, surveys and feedback processes that produce useful evidence.
• Use AI to organise, summarise, compare and visualise anonymised or synthetic data.
• Analyse open-ended responses to identify themes, misconceptions, emerging concerns and differences between groups.
• Configure chatbots or bounded agents to examine evidence, test assumptions, identify missing information and generate provisional insights.
• Combine different evidence sources while distinguishing description, correlation, interpretation, prediction and causation.
• Translate analytical findings into proportionate and responsible actions.
• Establish appropriate safeguards, human oversight and accountability.

Practical Learning Experience‍

The workshop will combine concise presentations, demonstrations, collaborative exploration, ethical dilemmas and hands-on activities. Participants will examine how multimodal and analytical AI can work with spreadsheets, written feedback, documents, images and mixed evidence. They will experiment with natural-language data analysis, compare AI-generated interpretations and evaluate the reliability of resulting visualisations and conclusions.

They will map sources of organic data within a school and consider how everyday learning activities can be designed to reveal student understanding, participation, misconceptions and support needs. Examples will include Singapore's SLS Data Assistant and other SLS capabilities, alongside school-approved analytical, research and productivity tools.

Participants will also configure a prototype analytical assistant or agent. Its design will specify its purpose, authorised data sources, analytical approach, expected outputs, limitations, prohibited actions and points requiring human review.

Ethics, Privacy and Responsible Governance

Ethical and governance analysis will be integrated throughout the workshop. Participants will address dilemmas involving sensitive student and staff information, profiling, automated recommendations, predictive indicators, bias, re-identification, surveillance, external AI providers and excessive reliance on algorithmic conclusions.

The workshop will consider purpose limitation, data minimisation, access controls, de-identification, retention, transparency, contestability, security and accountability. No identifiable school data will be required.

Short Concluding Case Study

At the end of the workshop, participants will apply the day's frameworks to a concise Singapore school scenario containing synthetic quantitative data, qualitative feedback and contrasting stakeholder perspectives. They will generate possible insights, challenge the AI's interpretation, identify ethical and privacy concerns, and propose an appropriately cautious response.

Participants will leave with a framework for responsible AI-supported leadership, a prototype analytical assistant, an organic-data map and a governance checklist applicable to their own setting. The workshop will connect with the EdTech Masterplan 2030, relevant SLS capabilities, Singapore data-protection guidance and responsible governance principles for agentic AI.

Who Should Attend

• Primary, Secondary School and JC/MI Educators