Authentic Assessment in the AI Era: What Educators Are Doing Now, What Comes Next

Brett Christie, Ph.D.
VP, Educational Innovation & Inclusivity

Our Alchemy webinar on November 18, 2025,  brought together almost 400 instructors, designers, and campus leaders who are advancing authentic assessment in practical ways. Our live Padlet captured quick snapshots of their current practices, near-term steps, and the kinds of career signals students can claim when assignments mirror real work. Below is a concise synthesis you can share with colleagues or use to inform your next course revision.

What instructors have already changed

Many participants reported steady movement from product-only grading to designs that make thinking visible and align with real contexts. Discussion also focused on the increased importance of this in light of AI.

• Process and feedback: staged drafts, peer review, revision memos, formative checks
• Real audiences and contexts: client briefs, advisory scenarios, stakeholder interviews, policy settings
• Multimodal artifacts: portfolios, one-page briefs, infographics, UX reports, debates, zines
• AI norms and reflection: explicit course levels for AI use, short disclosure prompts, show prompts and outputs, brief metacognitive notes on what the student did versus what the tool did
• Scaffolding and transparency: clearer criteria and smaller steps that build toward a credible deliverable

Five quick moves participants plan to try next

Participants focused on low-lift changes that raise credibility and portability without overhauling a course. The emphasis is on clearer deliverables, visible process, and lightweight transparency around tool use. Following is a short-list of examples commonly stated:

1. Add an AI-use reflection that distinguishes tool output from student reasoning
2. Tighten rubrics to real-world criteria, including purpose, audience, and quality markers
3. Name a real audience for an existing task and adjust tone and format accordingly
4. Add a short draft checkpoint with peer or instructor feedback
5. Curate artifacts into a minimal portfolio page or brief with a slide pitch

Career signals students can claim

Career signals are visible, verifiable cues that help employers interpret what a student can do. They translate coursework into named skills, authentic deliverables, and brief context about audience, tools, and constraints, so an artifact travels beyond the class. In practice, a strong signal looks like a portfolio item or resume line with a linked product, a one-sentence outcome or competency, and a note on method or validation (e.g., reviewed by an external partner).

• Policy brief to a city board or campus office with a short pitch
• Stakeholder interview summary with recommendations
• UX test report with annotated screenshots and a findings table
• Data analysis memo that separates AI-generated content from student interpretation
• Portfolio set that pairs product with a short reflection on career choices and trade-offs

Try this in your next revision cycle

If you have time to change only one or two elements, prioritize moves that make the deliverable explicit, the process visible, and the integrity signals clear. These small adjustments compound quickly and do not require new platforms or major rewriting.

• Convert one major assignment into a client or community brief with a named audience and purpose.
• Add a draft checkpoint with quick peer or instructor feedback plus a 100-word revision memo.
• Include two reflection prompts on tool use: What, if anything, did you generate with AI? What did you keep, modify, or discard, and why?
• Edit or create two rubric rows tied to workplace criteria (purpose/audience and quality of evidence or decision).
• Ask students to submit a minimal portfolio artifact (e.g., one-page brief and 3-slide pitch) suitable for sharing beyond the course.

What is coming next in the series

We continue this work on December 10 at 12 p.m. ET with “Strategies for Connecting Learners to the Workplace,” a campus leadership session on where and how to credential learning, how to engage employers through advisory sprints, challenge briefs, and artifact review, and which governance and data practices help teams scale what works. Join us to leave with a practical starter kit to pilot a pathway, align stakeholders, and connect coursework to workplace needs.