Assessment

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Being the Town Crier: Nothing Matters But Instructional Redesign
Being the Town Crier: Nothing Matters But Instructional Redesign

We need more experiential learning, debating, PBL, and portfolio development (especially because degrees are no longer substantial signifiers of capability) and less (not zero) long-form writing….

Reduce the overload of writing and put real weight behind verbal communication. Students need far more time speaking, debating, presenting, and defending ideas both in and beyond class.

• Guarantee semester-long introductory courses in computer science and AI. If schools can’t staff them, run them online. Add robotics and cyber security so every student understands the systems shaping their future.

• Expand elective options and award academic credit for debate, Model UN, and other high-value academic clubs. These are the environments where students really learn to think and develop an understanding of what is going on in the world.

• Build strong entrepreneurship pathways and push students to use them. Make launching a small business a graduation requirement so every student gets experience creating value instead of just completing assignments.

• Partner with local businesses to develop hands-on experiential learning and certification programs. Students need credentials tied to real workplaces, not just classroom seat time.

·stefanbauschard.substack.com·
Being the Town Crier: Nothing Matters But Instructional Redesign
Another AI Side Effect: Erosion of Student-Teacher Trust (Greg Toppo)
Another AI Side Effect: Erosion of Student-Teacher Trust (Greg Toppo)

teachers can lessen the allure of taking shortcuts by solving for these conditions — figuring out, for instance, how to intrinsically motivate students to study by helping them connect with the material for its own sake. They can also help students see how an assignment will help them succeed in a future career. And they can design courses that prioritize deeper learning and competence. To alleviate testing pressure, teachers can make assignments more low-stakes and break them up into smaller pieces. They can also give students more opportunities in the classroom to practice the skills and review the knowledge being tested. And teachers should talk openly about academic honesty and the ethics of cheating. “I’ve found in my own teaching that if you approach your assignments in that way, then you don’t always have to be the police,” he said. Students are “more incentivized, just by the system, to not cheat.” With writing, teachers can ask students to submit smaller “checkpoint” assignments, such as outlines and handwritten notes and drafts that classmates can review and comment on. They can also rely more on oral exams and handwritten blue book assignments.

·larrycuban.wordpress.com·
Another AI Side Effect: Erosion of Student-Teacher Trust (Greg Toppo)
What Counts as Cheating with AI? Teachers Are Grappling with How to Draw the Line (Howard Blume and Jocelyn Gecker)
What Counts as Cheating with AI? Teachers Are Grappling with How to Draw the Line (Howard Blume and Jocelyn Gecker)

The Stanford researchers concluded that cheating was common before AI — and it remains so. It is the nature of cheating that is evolving.

“This year’s data is showing a decline in copying off a peer and it seems there is more use of AI instead,” said Lee, an associate professor at the Stanford Graduate School of Education.

In these surveys, about 3 in 4 students reported behaviors in the last month that qualify as cheating, figures similar to what was reported prior to AI.

·larrycuban.wordpress.com·
What Counts as Cheating with AI? Teachers Are Grappling with How to Draw the Line (Howard Blume and Jocelyn Gecker)
AIAS Translations
AIAS Translations
AIAS Translations Thanks to our community of educators, we are able to share translations of the AIAS in a range of languages. Buttons link to previews, downloads, or editable originals. If you don…
·aiassessmentscale.com·
AIAS Translations
The wicked problem of AI and assessment
The wicked problem of AI and assessment

Our findings demonstrate that the GenAI-assessment challenge exhibits all ten characteristics of wicked problems. For instance, it resists definitive formulation, offers only better or worse rather than correct solutions, cannot be tested without consequence, and places significant responsibility on decision-makers. In the light of this redefinition of the AI and Assessment problem, we argue that educators require certain institutional permissions – including permission to compromise, diverge, and iterate – to appropriately navigate the assessment challenges they face.

Compromise: It allows educators to state plainly that this assessment prioritizes X at the expense of Y, and here is why. It transforms institutional culture from one that punishes imperfection to one that learns from it. When we stop seeking perfect solutions, we can start having honest conversations about which trade-offs serve our students best, which failures taught us most, and how to be thoughtfully imperfect rather than accidentally inadequate.

Permission to Diverge: At its core, ‘permission to diverge’ means accepting that successful practices in one educational context need not – and often should not – be replicated elsewhere. It is the recognition that divergent approaches to common challenges can reflect contextual wisdom rather than inconsistency or failure. By granting ourselves permission to diverge, we acknowledge that different contexts might require quite different responses. This recognises that quality manifests differently across years, disciplines, cohort sizes, and professional destinations. The business educator who integrates AI because employers demand it and the nursing educator who restricts it to ensure clinical competence are both appropriate. Divergence can reflect wisdom that we can easily mistake for confusion. This permission transforms institutional expectations from uniformity to fitness for purpose. Divergence becomes a sign of thoughtful response rather than institutional failure.

Permission to iterate: When AI capabilities transform monthly, when student behaviours shift each semester, and when professional requirements evolve constantly, the result can be that educators design assessments for yesterday’s technology, implemented with today’s students, preparing for tomorrow’s unknowns. Permission to iterate recognizes that wicked problems evolve continuously, making fixed solutions obsolete.

The permission to iterate recognizes wicked problems evolve continuously, making fixed solutions obsolete. This permission transforms assessment from a product to be delivered to a practice to be refined.

The path forward requires abandoning the search for silver bullets in favour of developing adaptive capacity. This means creating institutional structures that support educator decision-making rather than mandating uniform responses, recognizing divergent approaches as evidence of contextual wisdom rather than institutional inconsistency, and treating assessment iteration as professional development rather than design failure.

Our findings demonstrate that the GenAI-assessment challenge exhibits all ten characteristics of wicked problems. For instance, it resists definitive formulation, offers only better or worse rather than correct solutions, cannot be tested without consequence, and places significant responsibility on decision-makers. In the light of this redefinition of the AI and Assessment problem, we argue that educators require certain institutional permissions – including permission to compromise, diverge, and iterate – to appropriately navigate the assessment challenges they face.
It allows educators to state plainly that this assessment prioritizes X at the expense of Y, and here is why. It transforms institutional culture from one that punishes imperfection to one that learns from it. When we stop seeking perfect solutions, we can start having honest conversations about which trade-offs serve our students best, which failures taught us most, and how to be thoughtfully imperfect rather than accidentally inadequate.
At its core, ‘permission to diverge’ means accepting that successful practices in one educational context need not – and often should not – be replicated elsewhere. It is the recognition that divergent approaches to common challenges can reflect contextual wisdom rather than inconsistency or failure. By granting ourselves permission to diverge, we acknowledge that different contexts might require quite different responses. This recognises that quality manifests differently across years, disciplines, cohort sizes, and professional destinations. The business educator who integrates AI because employers demand it and the nursing educator who restricts it to ensure clinical competence are both appropriate. Divergence can reflect wisdom that we can easily mistake for confusion. This permission transforms institutional expectations from uniformity to fitness for purpose. Divergence becomes a sign of thoughtful response rather than institutional failure.
When AI capabilities transform monthly, when student behaviours shift each semester, and when professional requirements evolve constantly, the result can be that educators design assessments for yesterday’s technology, implemented with today’s students, preparing for tomorrow’s unknowns. Permission to iterate recognizes that wicked problems evolve continuously, making fixed solutions obsolete.The permission to iterate recognizes that wicked problems evolve continuously, making fixed solutions obsolete.
This permission transforms assessment from a product to be
This permission transforms assessment from a product to be delivered to a practice to be refined
The path forward requires abandoning the search for silver bullets in favour of developing adaptive capacity. This means creating institutional structures that support educator decision-making rather than mandating uniform responses, recognizing divergent approaches as evidence of contextual wisdom rather than institutional inconsistency, and treating assessment iteration as professional development rather than design failure.
·tandfonline.com·
The wicked problem of AI and assessment
AI and Higher Ed: An Impending Collapse (opinion)
AI and Higher Ed: An Impending Collapse (opinion)

Herein lies the trap. If students learn how to use AI to complete assignments and faculty use AI to design courses, assignments, and grade student work, then what is the value of higher education? How long until people dismiss the degree as an absurdly overpriced piece of paper? How long until that trickles down and influences our economic and cultural output? Simply put, can we afford a scenario where students pretend to learn and we pretend to teach them?

·insidehighered.com·
AI and Higher Ed: An Impending Collapse (opinion)
The AI Assessment Scale: New Peer Reviewed Paper
The AI Assessment Scale: New Peer Reviewed Paper
The AI Assessment Scale (v2) has officially been published in the open access Journal of University Teaching & Learning Practice (JUTLP). Read more here!
·leonfurze.com·
The AI Assessment Scale: New Peer Reviewed Paper
Tallyrus
Tallyrus
Tallyrus lets you upload documents and create custom rubrics to instantly analyze reports, resumes, or essays, turning hours of review into minutes.
·tallyrus.com·
Tallyrus
The College Essay is dead
The College Essay is dead
Why not ask the AI to do the face-to-face assessment? Let’s call this an AI Viva, by analogy with the verbal (viva voce) thesis defence traditionally reserved for PhD students. Here is how I think an AI viva could work: students continue to be taught in class, with as much interaction with the professor as possible; they can use AI as much as they like during the course, using it as a personal teaching assistant (a role it already plays very well). But at the end of the course, the student must meet with an AI examiner - without notes or help - and show what they have learned.
Why not ask the AI to do the face-to-face assessment? Let’s call this an AI Viva, by analogy with the verbal (viva voce) thesis defence traditionally reserved for PhD students. Here is how I think an AI viva could work: students continue to be taught in class, with as much interaction with the professor as possible; they can use AI as much as they like during the course, using it as a personal teaching assistant (a role it already plays very well). But at the end of the course, the student must meet with an AI examiner - without notes or help - and show what they have learned.
·ariesam.substack.com·
The College Essay is dead
Asking a More Productive Question about AI and Assessment
Asking a More Productive Question about AI and Assessment
'Given that AI exists in the world, and that students are likely to use it (whether accidentally or on purpose), what evidence of learning would I now find persuasive?'
·link.springer.com·
Asking a More Productive Question about AI and Assessment
The Un-Cheatable Assignment
The Un-Cheatable Assignment
Shifting from Policing Products to Assessing Process in the Age of AI
·purposefulai.substack.com·
The Un-Cheatable Assignment