Why responsible use of AI requires more than rules
AI adoption is accelerating across industries, and learning development (L&D) teams and educators in K-12 and higher education are racing to design training and instruction that enables employees and students to effectively use new tools. This is meaningful and important work. However, addressing overlooked but important differences can guide how curricula and training programs are designed. When supporting AI upskilling and coaching its use, it is essential to distinguish between ethics and integrity. Although closely related, they are not the same. By explicitly addressing this difference, educators can equip learners with the mindsets and behaviors needed to use AI responsibly and successfully.
Many organizations and institutions are launching AI ethics modules or units that introduce principles such as fairness, transparency, privacy, and responsible use. Ethics is a set of moral principles or values that guide how an individual or group thinks, decides, and acts. Teaching ethics helps AI users grapple with questions about what is “right” and “wrong” in human-AI interactions, and why those distinctions matter.
However, studying ethics alone cannot teach learners how to act with integrity when interacting with AI systems in real-world situations. If ethics outlines what is right, integrity reflects a determination to live by these principles with integrity and consistency. This distinction becomes mission-critical as organizations increasingly rely on AI-generated content, recommendations, predictions, and insights. Without integrity, even an ethical system can be abused. Without ethics, integrity has no compass.
In this article…
Ethics vs. Integrity: A Practical Distinction for L&D and Educators
ethics
Ethics refers to the standards, policies, and principles that govern the responsible use of AI, including:
Data privacy requirements Transparency and disclosure guidelines Expectations for accuracy verification Bias detection and mitigation Rules for fair and impartial use
Ethics provides rules for employees and students to follow when interacting with AI. For example, copying and pasting sensitive customer, employee, or student information into a large-scale language model (LLM) for data processing, performance evaluation, or grading can save time, but can lead to privacy violations if identifying information is not removed. There is also an ongoing debate across disciplines about whether inputting someone else’s work into an AI system raises copyright concerns. Additionally, reports generated by AI are subject to errors, creating risks not only for the users themselves, but for everyone affected by their decisions and ratings.
AI ethics guidance is often comparable to compliance training in the workplace or guidance on implementation policies and procedures in K-12 education. These approaches typically focus on establishing shared definitions, expectations, and a guiding framework. As a result, learners may leave with experience that allows them to define ethical AI use, but lack the behavioral fluency needed to consistently apply these principles in practice. Honesty is important here.
sincerity
Integrity refers to the daily habits, decisions, and actions individuals make when interacting with AI tools, including in instructional settings such as live tutoring, where AI can support learning without replacing human judgment. Demonstrating integrity AI users independently verify output, double-check sources, avoid blind trust, and take responsibility for errors. These are habits worth developing.
Developing integrity requires scenario-based practice. Educators and trainers may ask questions such as:
What do you do when the output of the AI seems useful but is questionable? What response to this AI use case shows good faith? Where does AI pose a risk to this workflow, study session, or assignment?
Individuals who develop integrity when it comes to using AI will act responsibly, even when they believe their work will not be challenged. Behaviors that demonstrate integrity include:
Choose not to copy and paste AI output without validating it. Be honest about how much AI is involved in your work. Report harmful or biased output. Avoid over-reliance on AI for decisions that require human judgment. Respect confidentiality even when AI tools tempt shortcuts.
Ethics is taught directly. Integrity develops over time and is shaped by experience, culture, and practice. Understanding this distinction is essential to determining the types of learning experiences that L&D professionals, curriculum designers, and educators must design to truly support learners.
Transparency regarding AI use
Most organizations now expect employees and students to disclose when they use AI. Without integrity-oriented behaviors, individuals may under-report AI assistance, hide errors, or misrepresent AI-generated work as their own. L&D professionals and educators must set ethical expectations for transparency and create conditions that encourage learners to practice it.
Learners must feel psychologically safe to disclose when and how they use AI, if the output is inaccurate, or if they are unsure how to verify the results. Psychological safety is enhanced when expectations of transparency are made explicit rather than remaining implicit. One practical way to do this is to provide a sample disclosure statement such as the following:
AI was used for brainstorming and ideation. The final work reflects the author’s unique ideas. AI was used to support preliminary research and question generation. All sources were independently identified and verified by the authors. AI was used to summarize source material. All summaries have been checked by the authors with their original sources. We used AI to draft sections based on our original notes and data. All output has been verified and corrected by the authors. Utilizes AI for editing and correction. All ideas are the author’s own. AI was used to provide feedback suggestions. The final revised version reflects the judgment and decisions of the authors. AI was used to generate presentation slides based on the author’s original content, edited for accuracy and clarity.
In all cases, the author is responsible for the content. Integrity regarding the use of AI will only exist if transparency is expected and supported. Once disclosure is normalized, employers, instructors, and reviewers will be able to better assess whether learning objectives are being met and determine when follow-up is needed.
As AI becomes more prevalent in workplaces and classrooms, addressing ethics and integrity will require not only clear policies but also removing the stigma against honest reporting of AI use. In the spirit of transparency, we hereby disclose that ChatGPT was used to support editing and revision during the brainstorming process for this article. The ideas and arguments presented are my own.
How to teach ethics and integrity
1. Incorporate ethics and integrity into skill maps and competency frameworks
L&D teams and educators should embed AI ethics and integrity directly into skill maps and competency frameworks and label them as explicit competencies within modules, lessons, and assessments. When these terms appear in learning objectives, activity descriptions, and assessment criteria, they are much more likely to be taught, practiced, and evaluated than if they are treated as background principles.
2. Distinguish between ethical principles and honest behavior
Learners should practice distinguishing between ethical principles (e.g., AI output should be verified) and integrity actions (e.g., matching summaries to source documents). Simple activities such as sorting, matching, and labeling scenarios can help reinforce this distinction.
3. Design micropractice moments
In addition to dedicated instruction on AI ethics and integrity, L&D teams and educators can enhance learning by embedding short repetitive practice moments throughout existing learning experiences. These can be incorporated into onboarding programs, leadership pathways, compliance refreshers, project-based learning, as well as classroom routines and early coursework in K-12 education. Micropractice moments may include asking learners to correct biased responses generated by AI, identify privacy or accuracy risks posed by the use of AI, pause and check the source before relying on output generated by AI, and more. By incorporating these moments into regular teaching and work processes, ethics becomes something that learners understand and integrity becomes something that is practiced. Over time, these small but consistent interventions can help build integrity into a habit rather than a one-time lesson.
4. Build a training scenario
Scenarios help learners connect ethics to action across work and learning contexts. For example, consider a situation where an AI assistant summarizes a collaborative project, discussion, or written assignment while minimizing or misrepresenting the contributions of team members or students. This is a risk that can disproportionately impact individuals from marginalized groups. Learners can identify relevant ethical principles and decide what integrity-based actions to follow.
5. Incorporate reflection questions
Regular reflection helps learners consider their use of AI, recognize when they may want to forego validation for convenience, and build stronger habits of critical evaluation. Reflection also encourages learners to consider how their assumptions shape their interpretation of AI output. L&D professionals and educators can encourage this reflection with targeted questions that highlight judgment, responsibility, and risk.
This AI What parts of the generated content did you examine, modify, or reject, and why? What evidence did you use to confirm or dispute the accuracy of this output? If this output were used as is, it would be ethical, practical, or What human risks might this pose? Who could be affected by errors, omissions, or bias in this output? If you were responsible for the results of this output, what would you change before sharing or sending it?
These questions help learners slow down, surface risks, and practice integrity-based decision-making in real-world situations. It also aligns with a broader framework of five key questions that help AI users validate output, surface assumptions, and maintain agency.
conclusion
As generative AI accelerates, organizations and learning institutions must provide guidance that addresses both ethics and integrity. Ethics establishes rules for responsible use. Consistency ensures that these rules are applied consistently in practice.
Ethics and integrity together form the basis of responsible AI use. Workplace learning, K-12, and higher education educators are in a unique position to not only provide learners with AI tools, but also equip them with the judgment to use them successfully.
