The Thering Framework
A guide supporting faculty in designing assignments and learning experiences that preserve critical thinking, creativity, and integrity in an era of generative AI.
Introduction
In Exercised (Lieberman, 2020), evolutionary biologist Daniel Lieberman shows that physical exercise, once built into daily survival tasks, now must be engineered into our lives through gyms, sports, and routines. The same is becoming true for mental exertion in the age of artificial intelligence.
Where once students had to summarize, restate, or draft ideas as essential steps in learning, tools like generative AI now automate many of those functions. Faculty rightly fear a process of deskilling—that students will lose the opportunity to practice critical thinking, creativity, empathy, and judgment if learning is reduced to outsourcing tasks to machines. As MIT's Your Brain on ChatGPT report (2023) highlights, heavy reliance on generative AI can shift cognitive load away from these core skills.
This guide begins from the premise that, just as physical fitness now requires intentional exercise, intellectual fitness now requires intentional learning design. Faculty must engineer environments that exercise students' thinking 'muscles' across five domains:
Core Cognitive Skills
Critical thinking, problem framing, decision-making, and transfer of learning.
Creative & Adaptive Skills
Creativity, flexibility, curiosity, and resilience in the face of challenges.
Metacognitive Skills
Reflection, self-regulation, judgment of learning, and learning agility.
Social & Ethical Skills
Collaboration, communication, perspective-taking, and civic reasoning.
Information & Integrity Skills
Information literacy, source evaluation, digital integrity, and misinformation awareness.
AI Risk Rubric for Learning Activities
This rubric helps faculty categorize assignments and activities based on their vulnerability to being completed primarily by AI tools. Risk levels apply to tasks, not to students.
| Risk Level | Indicators | Examples | Design Priority |
|---|---|---|---|
| Low | Requires live interaction, embodied practice, or personal experience. Involves unique context/data not in AI training sets. Student thinking is visible (oral, iterative, collaborative). | In-class debate, reflective journal, lab with unique data, improv warm-up. | Focus on authentic engagement and transparent criteria. |
| Partial | Some elements can be AI-assisted (summarizing, organizing, drafting), but core learning outcomes still require student judgment, context, or collaboration. AI can accelerate but not replace the task. | Case study analysis, concept mapping, literature review matrix, prototype design. | Add scaffolds that highlight reasoning and process, not just final product. |
| High | Task can be fully or mostly automated with AI without demonstrating intended learning. Product-oriented with minimal process visibility. | Restating problems, paraphrasing articles, first-draft summaries, simple comparative critiques. | Redesign tasks to surface process (multiple drafts, oral defense, reflection). |
Usage Notes
- This rubric applies to activities and assignments—not to students.
- A single assignment can shift categories depending on how it's structured (e.g., "compare two articles" is High risk if written only, but Partial if students must present and defend orally).
- Faculty should consider pairing High-risk tasks with Design Fixes to recover the intended skill practice.
Sample Assessment Rubric: Case Study Analysis
This sample rubric provides faculty with clear criteria for assessing student performance in a case study analysis. It focuses on critical thinking, reasoning, and evidence use—skills that can be partially AI-assisted but must ultimately be demonstrated by the student.
| Criteria | Beginning | Developing | Proficient | Advanced |
|---|---|---|---|---|
| Problem Identification | Problem is vague, misidentified, or missing. | Problem is identified but incomplete or lacks clarity. | Problem is clearly identified with some context provided. | Problem is precisely articulated with rich context and significance. |
| Use of Evidence | Little or no evidence; evidence is irrelevant or incorrect. | Some evidence provided but weakly integrated or partially relevant. | Relevant evidence is used appropriately to support reasoning. | Evidence is comprehensive, well-chosen, and integrated with strong reasoning. |
| Analysis & Reasoning | Analysis is superficial, descriptive only, or illogical. | Some reasoning is evident, but analysis is incomplete or partially flawed. | Sound reasoning with logical analysis of main issues. | Sophisticated reasoning; multiple perspectives and complexities explored. |
| Recognition of Trade-Offs | No recognition of alternative viewpoints or trade-offs. | Acknowledges alternatives but with limited explanation. | Considers trade-offs and alternatives with reasonable explanation. | Thoroughly evaluates trade-offs and integrates them into final conclusions. |
| Communication & Organization | Writing or presentation is unclear, disorganized, or difficult to follow. | Some organization but with lapses in clarity or flow. | Clear, organized communication with logical flow. | Exceptional clarity, organization, and engagement in communication. |
Usage Notes
- This rubric can be adapted for written, oral, or group case study assignments.
- Faculty may assign point values to each level (e.g., 1–4) to generate a total score.
- Transparency: Share the rubric with students in advance to guide their preparation.
- AI Risk Consideration: To reduce AI overuse, require students to annotate reasoning steps or present orally.
Sample Assessment Rubric: Fact-Checking Assignment
This sample rubric supports faculty in assessing a fact-checking assignment. The focus is on students' ability to evaluate information, document their evidence trail, and reflect on challenges in verifying claims.
| Criteria | Beginning | Developing | Proficient | Advanced |
|---|---|---|---|---|
| Evidence Trail | Little or no documentation of sources; evidence missing or irrelevant. | Some sources documented but incomplete or inconsistently cited. | Complete evidence trail showing relevant, credible sources. | Comprehensive, transparent evidence trail with annotations explaining source choices. |
| Accuracy of Verification | Verification is incorrect or absent; claims remain unsubstantiated. | Some claims verified correctly, but errors or gaps remain. | Most claims accurately verified with credible sources. | All claims verified accurately with triangulated, high-quality sources. |
| Source Evaluation | No evaluation of credibility; sources used uncritically. | Some evaluation of credibility but superficial or incomplete. | Credibility of sources evaluated with relevant criteria (e.g., author, bias, accuracy). | Sophisticated evaluation of source credibility with clear justification. |
| Reflection on Challenges | No reflection or minimal comments on the process. | Some reflection on difficulties but lacks depth or specificity. | Thoughtful reflection on verification challenges and how they were addressed. | Deep, critical reflection on verification challenges, strategies, and lessons for future practice. |
| Communication & Clarity | Work is unclear, poorly structured, or difficult to follow. | Some organization, but lapses in clarity or coherence. | Clear, organized presentation of fact-checking process. | Exceptionally clear, engaging, and well-structured communication of findings. |
Usage Notes
- This rubric can be applied to written reports, presentations, or digital portfolios.
- Faculty may assign point values (e.g., 1–4) to generate a score for each criterion.
- Transparency: Share with students to clarify expectations of thorough verification and integrity.
- AI Risk Consideration: Require students to document process steps (screenshots, annotations) that AI cannot replicate.
Accessibility & Universal Design for Learning
This addendum ensures that all activities and assignments are inclusive and equitable. Universal Design for Learning (UDL) principles help provide multiple means of engagement, representation, and expression so that all learners can demonstrate the intended skills.
Core Principles
Multiple Means of Engagement
Offer options for live, asynchronous, and online participation.
Multiple Means of Representation
Provide materials in accessible formats (captioned videos, screen-reader-friendly docs, alt-text for images).
Multiple Means of Expression
Allow students to demonstrate skills in different formats (written, oral, multimedia).
Design Considerations for High-Risk Activities
Debates
Barrier: Live debates may exclude students with speech, hearing, or anxiety-related disabilities.
Alternative: Allow asynchronous video or written debates, with peers responding over time.
UDL Note: Assess reasoning and evidence use, not speed or performance style.
Service-Learning
Barrier: Not all students can travel off-campus or engage in community placements.
Alternative: Offer virtual service-learning or campus-based engagement projects.
UDL Note: Assess civic reasoning and reflection rather than physical presence.
Improv / Role-Play
Barrier: Embodied, live role-play may exclude students with mobility or processing differences.
Alternative: Allow simulation-based or text-based role-play using online platforms.
UDL Note: Assess adaptability and perspective-taking, not performance delivery.
Fieldwork / Data Collection
Barrier: Some students cannot travel or safely access certain field sites.
Alternative: Use publicly available datasets, virtual labs, or simulations.
UDL Note: Assess data literacy and analysis rather than physical data collection.
Oral Presentations
Barrier: Live presentations may disadvantage students with speech or hearing disabilities.
Alternative: Allow pre-recorded videos, audio presentations, or accessible slide decks with transcripts.
UDL Note: Assess clarity, organization, and reasoning, not delivery style alone.
Implementation Tips
- Include accessibility statements in syllabi and assignment sheets.
- Provide clear rubrics that evaluate skills rather than format of delivery.
- Consult with Disability Services and use institutional accessibility checklists.
- Normalize choice by framing alternatives as equally rigorous, not as accommodations.
Privacy & Ethics Note
Some assignments ask students to engage with community members, collect data, or document online activity. These activities raise important considerations related to privacy, ethics, and compliance with laws such as FERPA (U.S.) or GDPR (Europe).
Key Considerations
- Informed Consent: If students interview or observe others, ensure participants provide informed consent. Faculty should supply sample consent forms and clarify voluntary participation.
- Anonymization & Redaction: Student submissions should remove or mask identifying details (names, photos, contact info). Screenshots should be redacted to show only relevant evidence.
- FERPA Compliance: Faculty must avoid requiring students to disclose personal educational records in public forums or shared assignments.
- GDPR & Data Protection: In courses with international students, faculty must ensure compliance with GDPR principles (minimization, consent, purpose limitation).
- Use of AI Tools: When assignments involve AI, clarify what student data may be exposed. Encourage institutionally approved tools and prohibit sharing sensitive data with third-party systems.
Implementation Practices
- Provide students with a brief Ethics & Privacy Guide at the start of the course.
- Require reflective notes on how students addressed privacy/ethics concerns in assignments.
- For projects involving human participants, seek guidance from IRB or equivalent committees.
- For online projects, focus on analyzing publicly available content rather than data scraping or surveillance.
Sample Faculty Language
Discipline-Specific Inserts
While the activities in this guide are broadly applicable, faculty often need to see examples in their own disciplines. The following provides 2–3 sample activities per domain for four broad areas. These are meant as adaptable models, not prescriptions.
Domain 1: Core Cognitive Skills
Reasoning, problem framing, decision-making, and transfer of learning. Activities are designed to help students practice critical thinking and structured judgment—skills that may be partially supported but not replaced by AI.
- Generate a list of debate topics relevant to [discipline].
- Suggest scaffolds to help novice students prepare for a debate.
- List case study scenarios in [discipline] that require ethical decision-making.
- Suggest reflection questions that encourage deeper analysis of a case.
- Provide examples of common misinformation in [discipline].
- Suggest questions students could use to test the reliability of a source.
- Suggest strategies for training students to verbalize their reasoning.
- List common reasoning errors in [discipline] students should avoid.
Domain 2: Creative & Adaptive Skills
Creativity, flexibility, curiosity, and resilience. Activities help students generate original ideas, adapt to uncertainty, and practice divergent thinking. While AI tools can assist with brainstorming, authentic creativity requires personal expression, context sensitivity, and reflective iteration.
- Suggest improv scenarios in [discipline] that would stretch adaptability skills.
- Generate strategies to assess creativity without bias toward performance style.
- List redesign challenges suitable for [discipline].
- Suggest reflection questions to help students explain their creative process.
- Provide journal prompts that spark curiosity in [discipline].
- Suggest ways students can connect journal entries to broader course themes.
- List historical or disciplinary cases that highlight resilience in [discipline].
- Suggest reflection prompts to help students connect case lessons to their own learning journey.
Domain 3: Metacognitive Skills
Reflection, self-regulation, judgment of learning, and learning agility. While AI can support by providing feedback or generating study aids, authentic metacognition requires students to evaluate their own strategies, progress, and decisions.
- Generate reflective journal prompts that encourage metacognition in [discipline].
- Suggest ways to help students compare self-assessment with external feedback.
- List reflection questions students should answer in an exam wrapper.
- Suggest ways to connect exam wrapper insights to future study strategies.
- Generate examples of measurable learning goals in [discipline].
- Suggest reflection prompts for midterm goal review discussions.
- List common ineffective study strategies in [discipline].
- Generate reflection prompts for students to evaluate the success of their own strategies.
Domain 4: Social & Ethical Skills
Collaboration, communication, perspective-taking, and civic reasoning. While AI tools can model communication, authentic learning in this domain requires human interaction and reflection on ethical consequences.
- Suggest group project topics in [discipline] that require collaboration.
- Generate peer evaluation questions to assess contributions fairly.
- List current ethical dilemmas in [discipline] that students could debate.
- Generate discussion questions that help students explore multiple perspectives.
- Provide stakeholder scenarios in [discipline] where perspective-taking is key.
- Suggest reflection prompts to help students analyze what they learned from role adoption.
- List civic engagement projects that connect to [discipline].
- Suggest reflection prompts for connecting civic engagement to course learning outcomes.
Domain 5: Information & Integrity Skills
Information literacy, source evaluation, digital integrity, and misinformation awareness. While AI tools can generate summaries or suggest sources, authentic skill development requires students to evaluate, cross-check, and reflect on information independently.
- List examples of mixed-quality sources in [discipline] for students to rank.
- Generate reflection prompts for students to explain how they judged credibility.
- List common misinformation themes in [discipline].
- Suggest ways students can triangulate information from multiple sources.
- Provide examples of strong annotation language in [discipline].
- Suggest reflection prompts for evaluating source usefulness.
- List current digital integrity issues relevant to [discipline].
- Generate discussion questions that connect digital ethics to professional practice.
References
The following references provide foundational research and frameworks that support the design of this Faculty Guide, demonstrating alignment with established scholarship in higher education, assessment, metacognition, information literacy, and teaching in the age of AI.
- 1American Association of Colleges and Universities (AAC&U). (2009). VALUE Rubrics. https://www.aacu.org/value-rubrics
- 2Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive–developmental inquiry. American Psychologist, 34(10), 906–911.
- 3Lieberman, D. E. (2020). Exercised: Why Something We Never Evolved to Do Is Healthy and Rewarding. Pantheon.
- 4MIT Media Lab. (2023). Your Brain on ChatGPT: Risks and opportunities of generative AI for cognition. Massachusetts Institute of Technology.
- 5National Research Council. (2012). Education for Life and Work: Developing Transferable Knowledge and Skills in the 21st Century. National Academies Press.
- 6Pintrich, P. R. (2002). The role of metacognitive knowledge in learning, teaching, and assessing. Theory Into Practice, 41(4), 219–225.
- 7Stanford History Education Group. (2019). Civic Online Reasoning curriculum. https://cor.stanford.edu
- 8Weimer, M. (2013). Learner-Centered Teaching: Five Key Changes to Practice (2nd ed.). Jossey-Bass.
- 9Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70.
Pilot & Evaluation Plan
To ensure the effectiveness of the Faculty Guide, a structured pilot and evaluation plan provides a roadmap for testing selected activities, gathering data, and refining materials before broader adoption.
Pilot Goals
- Test a representative set of activities across the five skill domains.
- Gather faculty and student feedback on clarity, usability, and effectiveness.
- Identify challenges in implementation (time, resources, accessibility).
- Assess impact on student engagement and skill development.
Pilot Sites & Participants
- Select 2–3 departments with diverse disciplines (e.g., STEM, Social Sciences, Humanities).
- Recruit volunteer faculty (5–10) willing to integrate at least two activities.
- Include a range of course levels (introductory, advanced) to test adaptability.
Evaluation Methods
- Faculty Surveys & Focus Groups: Collect feedback on ease of integration, student response, and observed learning outcomes.
- Student Feedback Surveys: Anonymous reflections on which activities helped them think, create, or collaborate.
- Rubric-Based Assessment: Use the sample assessment rubrics to measure student performance.
- Learning Artifacts Review: Analyze a sample of student work products for evidence of skill development.
Metrics for Success
Timeline
Semester 1
Recruit faculty, select activities, provide orientation.
Semester 2
Implement pilot, collect midterm and end-of-semester data.
Semester 3
Analyze results, refine guide, prepare for broader dissemination.
Next Steps
- Share pilot results with faculty senate or teaching and learning center.
- Revise the guide based on findings.
- Develop professional development workshops to support adoption.
- Consider publishing results in a teaching and learning journal for peer validation.