A Self-Assessment Tool for Teachers: Developing Self-Awareness of Instructional Values and Cheating
Introduction
This assessment tool1 is designed to help classroom teachers in the privacy of their desk chair reflect on their perspectives regarding academic integrity in the age of generative AI. I developed it from a variety of sources in the periphery, including a full load of peer-reviewed studies on teacher noticing from 1994-2018 in a Claude project. I did not find any tools like this one though my searches have been limited to open access material. So I can’t report on convergent or divergent validity and reliability, which is just to say I don’t recommend anyone take it up as a scientifically developed tool.
To be truthful, this is drafty. I would love constructive feedback. The instrument is not meant to identify "right" or "wrong" approaches, but to provide a dialogic space for teachers to talk to themselves from different ethical frameworks normally not salient in their minds. Every one of us is blessedly biased without which we could not function. The important point is to become aware of our biases so we can control them, not let them control us. Of course, share it widely. Use it as the basis for a collegial workshop. Nothing would please me more. I would love to get feedback from you.
Core Assumption 1: All teachers have a moral and ethical objection to academic dishonesty. This self-assessment tool acknowledges that different approaches to addressing loss of integrity stem from equally valid ethical foundations and educational philosophies, not from a lack of concern for academic integrity.
Core Assumption 2: All teachers are susceptible to modeling behaviors they don’t want replicated by their students. For example, the teacher who uses an AI-generated assignment or worksheet and then bans AI for students is sending mixed messages. Fix one’s own house first.
Two central questions guide this assessment:
How do we define academic integrity in an era of AI-assisted learning?
What responses to potential breaches best support our commitment to authentic teaching and learning?
Instructions
Rate each statement using the following scale. Record your rating to sum them at the end of this section:
1 = Strongly Disagree
2 = Disagree
3 = Neutral
4 = Agree
5 = Strongly Agree
Section A: Justice and Accountability Perspective
Clear boundaries between acceptable and unacceptable use of AI tools are essential for maintaining academic standards. ___
Students should be held individually accountable for distinguishing between legitimate assistance and dishonest shortcuts. ___
Consistent consequences for academic dishonesty demonstrate fairness to students who maintain integrity. ___
The integrity of assessment processes requires transparent standards for what constitutes original work. ___
Meaningful teaching cannot occur when students misrepresent their capabilities or efforts. ___
Establishing clear ethical guidelines for AI use prepares students for professional environments with similar expectations. ___
Students who intentionally misrepresent AI-generated work as their own undermine the trust essential to the learning process. ___
Academic institutions have a responsibility to uphold standards that distinguish between collaboration and dishonesty. ___
Clear expectations and boundaries around AI use help students develop discernment and ethical judgment. ___
Meaningful assessment requires confidence that students honor academic integrity. ___
TOTAL: _____
Section B: Beneficence and Development Perspective
Academic integrity concerns can signal opportunities to better support students' learning processes. ___
Technological changes require educators to continuously reexamine the definition of authentic learning. ___
Addressing academic dishonesty effectively requires understanding the contextual factors influencing student choices. ___
Educational approaches that emphasize growth over punishment better prepare students for ethical decision-making. ___
Developing students' intrinsic motivation for learning reduces the appeal of academic shortcuts. ___
Creating assessments that emphasize process and application over reproduction reduces opportunities for academic dishonesty. ___
Helping students understand the purpose behind assignments better supports integrity than focusing on detection and penalties. ___
Academic integrity is best fostered through building supportive learning communities rather than surveillance systems. ___
The most effective response to academic dishonesty addresses both the incident and the student's developmental needs. ___
In a rapidly changing technological infosphere, teaching adaptable ethical reasoning is more valuable than rigid rules which camouflage gray areas. ___
TOTAL: _____
Scoring
Section A Total: ___/50
Section B Total: ___/50
Framework Interpretation
Your scores reflect tendencies toward different ethical frameworks, not absolute positions:
Justice and Accountability Framework (Section A)
This perspective emphasizes:
Deontological ethics (principle-based reasoning)
Procedural fairness and consistency
Clear boundaries and standards
Individual responsibility and accountability
Maintaining institutional integrity
Beneficence and Development Framework (Section B)
This perspective emphasizes:
Consequentialist ethics (outcome-focused reasoning)
Educational development and growth
Contextual understanding and flexibility
Community and relationship-building
Systemic adaptation and innovation
Profile Descriptions
Justice-Oriented (A: 35+, B: <30) You approach academic integrity through principles of fairness and accountability. You value clear boundaries and consistent standards. Your ethical framework prioritizes maintaining the integrity of educational processes and ensuring equitable treatment of all students. This approach provides students with explicit guidelines and helps prepare them for professional environments with similar expectations.
Development-Oriented (A: <30, B: 35+) You approach academic integrity through a lens of student development and educational purpose. You value understanding context and creating conditions that naturally encourage integrity. Your ethical framework prioritizes student growth and adaptation to changing technological realities. This approach helps students develop intrinsic motivation and ethical reasoning skills that transfer beyond specific rules.
Integrated (A: 35+, B: 35+) You balance accountability with development, recognizing the complementary nature of both approaches. Your ethical framework draws from multiple traditions, allowing you to adapt your response based on specific circumstances while maintaining consistent principles. This approach combines clear expectations with supportive interventions, addressing both immediate concerns and long-term educational goals.
Exploratory (A: <30, B: <30) You may be actively reconsidering traditional approaches to academic integrity in light of technological changes. Your responses suggest openness to emerging frameworks that address new challenges in academic integrity. This position of inquiry can lead to innovative approaches that transcend traditional dichotomies between accountability and development.
Balanced (Both sections 30-34) You maintain a measured approach that draws elements from both frameworks depending on context. Your ethical reasoning shows flexibility while maintaining core principles about academic integrity. This balanced perspective allows for nuanced responses that consider both individual circumstances and institutional standards.
Application Strategies
For Justice-Oriented Educators:
Develop clear AI usage policies with explicit examples
Create assessment rubrics that specify acceptable AI contributions
Implement transparent documentation processes for AI assistance
Design assessments with authentication mechanisms
Establish consistent response protocols for potential violations
For Development-Oriented Educators:
Redesign assessments to emphasize process and application
Incorporate reflection on AI use into assignments
Create collaborative discussions about ethical technology use
Implement formative feedback approaches that support learning
Develop mentoring relationships that address underlying factors
For Integrated or Balanced Educators:
Create tiered response systems with both supportive and accountability elements
Design assignments that teach ethical AI use while maintaining standards
Implement educational interventions before punitive measures
Develop classroom communities that value both integrity and growth
Balance detection methods with redesigned assessments
Reflection Questions
How might your perspective on academic integrity influence your assessment design?
What tensions do you experience between maintaining standards and supporting student development?
How has your understanding of academic integrity evolved with technological changes?
What ethical principles most strongly inform your approach to academic integrity?
How might students with different learning needs experience your approach to academic integrity?
What institutional supports would help you implement your preferred approach?
Case Studies for Application
Case Study 1: The AI-Enhanced Essay
A student submits an essay that appears to blend their own ideas with AI-generated content. Most of the actual writing seems to belong to the student with occasional flourishes of verbiage. The quality is inconsistent, with fairly decent analysis in some sections and more basic reasoning in others. The student has not documented their use of AI, though your policy requires disclosure.
Consider:
What assumptions might you make about the student's intent?
How would you determine which parts are the student's original work?
What would a response that balances accountability and development look like?
How might this situation inform your future assignment design?
Case Study 2: The Collaborative Problem-Solving Problem
In a mathematics course, you notice several students have identical solution paths for complex problems, though the final presentations differ through variations in notation, explanation depth, computational steps, verification methods, and presentation style. When questioned, they explain they used an AI tool to help understand the approach, then worked through the problems themselves. Your syllabus prohibits unauthorized assistance but doesn't specifically address AI tools.
Consider:
How does this scenario challenge traditional definitions of "unauthorized assistance"?
What learning do you think did not take place by the decision to use AI to help understand the approach?
What learning do you think might not have taken place without AI assistance?
How might your response differ based on whether the students voluntarily disclosed their process?
What policy clarifications might prevent similar situations?
Case Study 3: The Case of Self-Reported Uncertainty
A student from another class approaches you expressing confusion about appropriate AI use. They admit to using AI to help brainstorm ideas for a paper and to edit their paper before turning it in, but they don’t know whether this behavior constitutes academic dishonesty. They found the brainstorming session helpful and expressed some excitement about the ideas they discovered. The edits didn’t seem so important, but a few grammar mistakes were fixed. But now they are having problems sleeping. They promise not to do it again.
Consider:
How does this disclosure from a student in another class influence your response?
What guidance would you provide to help this student navigate this ethical AI use crisis?
How might you use this interaction to address similar concerns with your students?
What does this student's uncertainty and anxiety reveal about current academic integrity frameworks?
Case Study 4: The Voluntary Disclosure of AI Use to Improve Coherence
A conscientious student schedules a meeting with you as her high school capstone experience mentor before final submission deadlines. Your job is to serve as an advocate for your assigned mentees as they complete these massive field studies their senior year.
Distraught, they admit to using an AI language model to check the coherence and flow of their lit review's near-final draft. The student explains they asked the AI to "identify any unclear transitions or logical gaps" in their argument but did not incorporate any AI-generated content or ideas. They were extremely careful.
They made revisions based on the coherence feedback, they have not submitted the final paper, but they now don’t know whether this constitutes academic misconduct under the school's policy, which prohibits unauthorized assistance but just vaguely mentions AI. It doesn't explicitly address AI use as a tool to improve coherence in an essay.
The student, who insists they do not regularly use AI for anything, wants to voluntarily report themselves and asks for a hearing with the Board of Magistrates to preemptively clear their name. They believe they are entitled to due process since their future is at stake. Taking action before being charged seems to them the proper course.
Consider:
How does the nature of this voluntary disclosure affect your perception of the student's intent and character?
Where might this specific use of AI fall on the spectrum between legitimate learning tool and unauthorized assistance?
What distinctions exist between using AI for coherence checking versus using traditional tools like grammar checkers or writing center feedback?
How might the panel's response to this case establish precedent for similar situations?
What opportunity does this present for clarifying institutional policies around generative versus substantive AI assistance?
How might voluntary disclosure discourage academic integrity and transparency from other students?
What would a response that honors both the student's honesty and the institution's academic standards look like?
How might this case inform the development of more nuanced policies that distinguish between different types of AI assistance?
I’ve been working on this project for over a month, burning far too much electricity to fuel Claude, Perplexity, and GPT mini-o3. I’m not nearly as fluent in bot models as I should be, and I hope I’m not harming the environment. How we can deceive ourselves! It’s safe to say that this work is all mine. Every word. Sometimes I was forced to use the same word the AI used to reference a concept because, well, it was truly the word I wanted to use. I may have stumbled onto it in an artificial text and for that, mea culpa. But I do reserve the right to use the word I want to use regardless of where the word came from. Case in point: Nuance. I used the word long before the bot got its hands on it, and I can have it if I want it. It’s also safe to say I could not have written this alone. The greatest support was slicing and dicing the core issues that bubble up in the crucible, deepening my understanding of them by marinating them in a revisable, kaleidoscopic synthesis of concepts from the teacher-noticing literature, finding items that at face value resonate and evoke an opinion. If I were developing this device as a scientific tool, it would need field trials, think alouds, interviews, statistical tests for construct validity vis a vis each ethical perspective, and much, much more. This scientific work, also, would be improved and carried out more efficiently with AI tools. I’m not sure what the future is for those of us who have integrated LLMs into our workflow. Should we boilerplate an explanation and use it in footnotes to acknowledge that we use LLMs? I can and do write without it—a lot—I love spiral notebooks and rely on them as my second brain. In fact, along with Floridi, I don’t see AI output as writing at all, but distilled linguistic probabilities organized by the rules and regs of traditional grammar as alchemized by computational language experts. Anyway, as always, I take full responsibility for this post and remain hopeful it can do some good somewhere. Keep the faith!