AI-Aware Learning Design Lab
Spring 2026 Workshop Series
This workshop series supports faculty and academic staff in designing learning experiences for AI-aware environments while keeping authentic thinking, disciplinary judgment, and productive learning friction at the center.
Rather than focusing on tools, the series starts with learning design fundamentals. Participants examine alignment between learning outcomes, assessment, and classroom activities in light of how students now study, read, write, and problem solve. The aim is not full course redesign, but identifying where learning is at risk and making one intentional, pedagogically grounded change.
The series is modular. Participants may attend individual sessions or complete the full sequence.
AI supported environments, whenever introduced, are treated as optional planning and diagnostic tools, not requirements.
Sessions
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This workshop helps faculty and academic staff examine assessments and assignments in light of changing student learning habits in AI-aware environments. Participants apply principles of learning-outcome-driven design to identify where learning may be lost, automated, or hidden, and where disciplinary reasoning and judgment must remain visible.
Faculty will leave with clarity about one assessment or assignment where learning is at risk and what kind of change would better support authentic thinking.
Required Level of Expertise
No prior AI experience required.
Key Objectives
- Apply principles of goal-oriented learning design to analyze assessments and assignments
- Align learning outcomes with disciplinary ways of thinking
- Identify where assignments or assessments are vulnerable to automation or shortcutting
- Distinguish between procedural reasoning and final answers
- Identify one assessment or assignment where learning is at risk and outline a focused improvement
Disciplinary Lenses
STEM
- Procedural reasoning and decision points
- Model selection, assumptions, and constraints
- Debugging outputs and documenting process
Arts and Humanities and Social Sciences
- Interpretation, positionality, and argument construction
- Bias detection and epistemological limits
- Voice, originality, and synthesis across sources
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Building on insights from Session 1, faculty and academic staff will redesign one assignment with a focus on supporting authentic student engagement in building knowledge and skills. Participants will examine how assignment structure, guidance, and expectations shape student learning behaviors.
Faculty may choose to work with student partners or collaborate with colleagues to test assignment clarity, workload, and points where misunderstanding or misuse may occur. The emphasis is on strengthening student ownership of learning while identifying where additional challenge or support is needed to ensure equitable access to expectations.
Prior Experience
Completion of Session 1 or familiarity with learning outcome-based design.
Teaching and Learning Design Mastery
- Applying principles of goal-oriented learning design, including backward design and Bloom’s taxonomy
- Designing assignments that foreground process, reasoning, and decision making
- Anticipating student learning behaviors in AI-aware environments
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This workshop focuses on designing meaningful mid-semester pulse checks that help faculty and academic staff better understand student learning experiences while a course is still in progress and make timely adjustments before final assessments. We will explore how pulse check data can inform small, targeted course adjustments that preserve appropriate challenge while reducing unnecessary barriers to learning.
Participants will examine how to design pulse check questions aligned with course learning outcomes and assessment expectations, including questions related to student understanding, workload, emotional experience, and engagement. A framework for support survey and aggregate data results with AI tools will be offered.
Required Level of Expertise
No prior AI experience required.
Teaching and Learning Design Mastery
- Understanding learner profile
- Designing pulse check questions that align with learning goals
- Interpreting student feedback through an equity-informed lens
- Making intentional mid-course adjustments
- Evaluating when AI-supported analysis adds value and when it does not
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This workshop helps faculty design class activities that actually produce the kind of thinking they want students to develop. Rather than adding more activities, participants will focus on choosing the right kind of engagement for their learning goals and understanding what students need in place for an activity to work.
Using Bloom’s taxonomy as a practical guide, participants will learn how to match learning outcomes with appropriate forms of engagement and how to identify the key preparation steps that support student success. Faculty will design or refine a short sequence of activities that support reasoning, interpretation, and decision-making in their own courses. Optional AI tools may be explored as planning and ideation supports.
Required Level of Expertise
Basic familiarity with active learning or participation in earlier sessions.
Participants Will Work On
- Using Bloom’s taxonomy to align engagement with learning outcomes
- Selecting activity types that match intended cognitive demand
- Identifying prerequisite knowledge and preparatory steps for activities
- Sequencing activities to scaffold thinking and participation
- Evaluating when optional AI-supported planning approaches add value
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This workshop reframes AI prompt design as a teaching strategy rather than a technical skill. Participants will explore how carefully designed prompts can make disciplinary ways of thinking visible to students by scaffolding planning, reasoning, critique, and reflection. Emphasis is placed on using prompts to help students evaluate the quality, limits, and assumptions of AI-generated output, not to generate answers.
The session focuses on low-risk, course-embedded uses that strengthen metacognition and disciplinary judgment.
Required Level of Expertise
Minimal technical expertise required. Focus is on teaching and learning design.
Teaching and Learning Design Mastery
- Use prompts to externalize thinking in a discipline
- Scaffold reasoning and metacognition through structured tasks
- Teach students to evaluate output quality based on reasoning and evidence
- Identify bias, assumptions, and hallucinations
- Clarify the boundary between tool-supported thinking and human judgment
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This workshop focuses on low-risk AI integrations that use instructor-curated, course-based materials within AI tools available to both faculty and students. Participants will design constrained AI environments that support student readiness by helping learners identify gaps in prerequisite knowledge, clarify key concepts, and practice reasoning through targeted problems with brief explanations, without replacing engagement with course materials.
Emphasis is placed on scaffolding comprehension and logical reasoning across levels of complexity and maintaining instructional control over content, prompts, and expectations.
Required Level of Expertise
Completion of Session 4 or experience with structured learning activities.
Teaching and Learning Design Mastery
- Scaffolding comprehension and reasoning using Bloom’s taxonomy
- Designing guided reading and analysis tasks that require evidence-based thinking
- Supporting authentic engagement through written and oral activities
- Using curated resources to reduce cognitive overload
- Designing learning environments that support thinking rather than shortcut