01
Inquiry framing
Define the question, relevant disciplinary knowledge, assumptions and success criteria. A teacher-configured SLS Learning Assistant can model Socratic questioning without supplying the final answer.
AI in Action: From Inquiry to Prototype
This practical workshop enables participants to experience how artificial intelligence can support an academic project from initial inquiry to the creation, evaluation and presentation of a final product. Participants will undertake the project from a student's perspective, using conversational and multimodal AI, research tools, data analysis, generative media and accessible digital-creation platforms within one coherent learning process.
The workshop will model an interdisciplinary project that secondary students could undertake in Singapore and that educators could subsequently reproduce or adapt. The emphasis will remain on academic learning: students must formulate questions, apply disciplinary knowledge, evaluate evidence, make decisions, document their reasoning and defend their conclusions.
The experience will demonstrate how AI-enabled features within the Singapore Student Learning Space (SLS) can contribute to different stages of the project, alongside other school-approved research, productivity, data-analysis and creative tools. All instructions, resources and collaborative workspaces will be provided through a dedicated workshop website.
01 — Context
Singapore Case Study
Participants will complete a structured case study based on an interdisciplinary academic project appropriate for Singapore secondary students. The project will address a question connected to Singapore's social, cultural, scientific, environmental or technological context and will require curriculum-related knowledge, research, data interpretation, consideration of contrasting perspectives and the development of an academic or digital response.
Materials will include background information, datasets, visual evidence, Singapore-based sources, academic requirements and project constraints. Participants will also consider how the project could be adapted for different subjects, levels and learning outcomes.
Objectives and Contents
- 01 Formulate an academically meaningful inquiry question.
- 02 Use multimodal AI to interpret texts, images, documents and data.
- 03 Conduct traceable research and verify AI-generated claims.
- 04 Distinguish evidence, interpretation, inference and speculation.
- 05 Use AI for questioning, feedback and iteration without replacing student thinking.
- 06 Create an academic or digital outcome using accessible tools.
- 07 Preserve evidence of student reasoning, authorship and decision-making.
- 08 Establish expectations for citation and disclosure of AI use.
- 09 Analyse accuracy, bias, privacy, transparency, equity and accountability.
- 10 Develop a project model transferable to a Singapore secondary classroom.
Practical Learning Experience
02
Research and evidence
Analyse the supplied materials, consult authoritative Singapore sources and use suitable research tools to locate and verify evidence, including data.gov.sg and other school-approved platforms.
03
Analysis and interpretation
Use spreadsheets, visualisation tools, multimodal AI or subject-specific applications to identify patterns, compare perspectives and develop evidence-supported conclusions.
04
Development and feedback
Produce an investigation, interactive model, visualisation, simulation, exhibition or prototype. Where enabled, SLS feedback assistants can support iterative improvement while collaborative spaces document discussion and decisions.
05
Critical review
Examine academic quality, AI inaccuracies, bias, accessibility and ethical concerns. Participants must identify what they accepted, modified or rejected.
06
Presentation and defence
Present the outcome and explain the evidence, decisions, AI contributions and limitations.
04 — Result
Learning Outcome
Participants will leave with a reproducible model for an AI-supported academic project grounded in Singapore's educational context. The workshop will demonstrate how MOE-provided tools can form part of a broader learning ecosystem while maintaining disciplinary rigour, student agency, responsible AI use and genuine intellectual ownership.
Who Should Attend
