Human learning at the center.

A platform for inquiry and practice

WeAI connects conceptual inquiry with practical learning experiences. Its research-informed approach brings together adult learning theory’s attention to experience and relevance, reflective practice’s emphasis on reconsidering action, and questions of judgment and agency in collaboration with AI.

The design focus is on how people interpret evidence, explore possibilities, make decisions, and retain responsibility for their work. Learning experiences provide concrete situations in which to articulate and revisit those choices.

Three interconnected capabilities

Smartly, Creatively, and Responsibly are mutually informing dimensions of human–AI collaboration. Critical judgment can guide exploration; new possibilities can prompt a reassessment of evidence; attention to consequences can reshape both. Learners draw on all three as the situation requires.

Smartly · Critical Judgment

Decide when AI is useful, compare claims with evidence, recognize uncertainty, and explain a decision.

Creatively · Purposeful Exploration

Develop alternatives, combine perspectives, and revise work while retaining human authorship and intention.

Responsibly · Accountable Action

Consider privacy, fairness, affected people, and the consequences of acting on an AI output.

From Knowing to Judgment to Action

This educational principle connects understanding with considered decisions and purposeful action. It describes an aspiration for learning, rather than a prescribed order for the three dimensions. Acting can reveal new questions; reflection can change a judgment; exploration can reveal what a learner needs to know next.

The current Practice module makes this relationship concrete: learners compare an AI summary with a fictional source, explain a decision, review prepared feedback, revise their response, and reflect on a new situation. The emphasis is on making reasoning visible and open to reconsideration.

Open research questions

• How do adult learners decide when to rely on, question, or decline an AI suggestion?
• How does explaining and revising a judgment relate to learners’ use of evidence in a new situation?
• How can AI-supported exploration encourage diverse ideas while preserving human authorship and agency?
• What helps learners recognize privacy, fairness, and accountability concerns before acting?
• How might confidence differ from demonstrated judgment, and how could future evaluation distinguish the two?

Proposed evaluation approach

Future evaluation could examine the reasoning learners provide, changes across revisions, and their responses to unfamiliar scenarios. Usability and accessibility would be considered alongside learning. Defined outcomes, appropriate methods, consent and privacy safeguards, and transparent reporting of limitations would guide this work.