AI Ethics: Academic Integrity and Citation
AI Literacy Framework Level 2 Use and Apply AI:
AI Ethics: Academic Integrity and Citation
Core Competencies covered in this chapter:
- Vigilance in use of AI tools and articulation of ethical use of AI in academic to uphold academic integrity.
- Transparency about AI usage in all academic work, including statements about and citation of AI-influenced work.
Introduction
Consider a scenario: A student submits a well-written essay that thoughtfully analyzes complex concepts. The ideas are sound, the arguments coherent, but you suspect AI involvement. Is this academic dishonesty? What if the student used AI as a brainstorming tool or for editing? These questions highlight the complexity of maintaining academic integrity in an era where the line between human and machine-generated content becomes increasingly blurred.
In this chapter, we will provide you with insights and research into how AI reshapes academic integrity, the challenges it presents, and strategies to uphold ethical academic work in higher education.
Interactive Module: AI Ethics: Academic Integrity and Citation
Reflect and Apply: Educator’s Toolkit
Core Competencies for Educators
Educators should teach students how to use AI tools ethically in academic work, including proper citation of AI-generated content and maintaining academic integrity. They should also understand, decide and explain institutional and course-level policies on AI use in class.
Reflection Questions
- How do the six fundamental values of academic integrity (honesty, trust, fairness, respect, responsibility, and courage) need to be reconsidered or reinterpreted in an era where AI tools can generate human-like content? Which value do you find most challenging to uphold in this context?
- Current research presents AI detection tools as unreliable for high-stakes academic decisions. What are the potential equity concerns when using these tools, and how might their use affect your relationship with students?
- Consider your own teaching practices: In what ways might your current assessment strategies unintentionally encourage students to use AI inappropriately? What specific changes could you make to design assignments that remain meaningful in an AI-augmented educational landscape?
- The concept of “post-plagiarism” suggests we need new frameworks for understanding academic integrity. How comfortable are you with the idea that “hybrid human-AI writing will become normal”? What boundaries would you establish between acceptable and unacceptable AI use in your discipline?
- What specific guidelines would you create for your students regarding AI citation and attribution? How would these guidelines balance transparency with practical considerations about the workload of documentation?
- AI misuse in academia is often a symptom of larger systemic pressures. Reflecting on your institutional context, what systemic issues might be driving students toward inappropriate AI use, and what role can you play in addressing these root causes?
- How might you use the framework of mastery-based learning (rather than performance-based assessment) to reduce the incentives for academic misconduct in your courses? What specific practices would you implement?
- Consider the “Artificial Intelligence Disclosure (AID) Framework” described in the module. How might implementing such a framework in your courses change your approach to assessment? What benefits and challenges do you foresee in requiring this kind of disclosure?Use the Padlet Discussion Board to share your thoughts with peer educators.
Tips and Best Practices
Streamlined Documentation Approaches
Students arrive at college without prior training in AI citation and acknowledgement, as this is not yet part of K-12 education. Just as we teach traditional citation methods, we need to explicitly teach AI citation as a new academic literacy skill. However, citation methods must balance thoroughness with practicality.
- Establish clear, reasonable documentation expectations: Provide templates and examples that demonstrate the necessary level of detail without requiring excessive documentation.
- Implement chat consolidation techniques: Teach students to organize their AI interactions within single conversation threads rather than multiple sessions. This makes sharing and citing much easier, as they can provide one comprehensive link rather than dozens of fragmented interactions.
- Use shared links for major platforms: For tools like ChatGPT that offer shareable links, show students how to generate and include these links in their documentation. This provides complete transparency without tedious manual copying.
- Create simplified templates for AI disclosure statements: Develop standardized forms, which asks students to document:
- Which AI tools they used
- How they used them (ideation, research, drafting, editing)
- Sample prompts they employed
- How they incorporated the AI-generated content
- Integrate process documentation into assignments: Build documentation into the workflow rather than treating it as an add-on task. For example, require an annotated first draft showing AI contributions alongside the final submission.
Implementation Strategies
- Demonstrate the documentation process in class, showing how you interact with AI and then document it.
- Provide exemplars of well-documented AI use to give students clear models to follow.
- Consider using a simplified version of the Artificial Intelligence Disclosure (AID) Framework appropriate to your course level.
- Create a course-specific AI policy that clearly articulates what constitutes appropriate AI use and documentation in your particular context.
- Emphasize transparency over perfectionism—make clear that honest disclosure, even if imperfect, is valued more than attempting to hide AI use.
- Gradually introduce more sophisticated documentation requirements as students develop proficiency, starting with basic disclosure and moving toward more comprehensive attribution practices.
