Ethics and AI in Teaching: Navigating the Complexities

Futuristic Robot in School Environment

Ethics and AI in Teaching: Navigating the Complexities

In today’s rapidly evolving educational landscape, artificial intelligence tools present both opportunities and significant ethical challenges for educators. As these technologies become increasingly integrated into classrooms, teachers must thoughtfully consider their implications.

Confirmation bias, for example, is a significant challenge. When students use AI tools, they often phrase queries in ways that lead to responses confirming their preexisting beliefs. As noted by Atos researchers, “This isn’t a flaw of the GenAI itself but a result of how we interact with it… steer[ing] the conversation to echo our existing beliefs and overlook counterarguments” (Atos, 2024). This cognitive bias can limit critical thinking rather than enhance it.

The academic integrity concerns are substantial. According to a KPMG study, “65 percent [of students] say they feel that they are cheating when they use generative AI” and “63 percent worry they will get caught” (KPMG, 2024). However, contrary to fears of widespread AI-enabled cheating, Turnitin data suggests only about 3% of student work is mostly AI-generated, indicating the issue may be less pervasive than feared but still requires attention.

For educators navigating these challenges, Swindell et al. (2024) propose “a framework for assessing the use and ethics of AI in modern education contexts regarding human versus AI generated textual and multimodal content”. This academic approach encourages thoughtful implementation rather than blanket rejection or acceptance.

Carnegie Learning offers practical guidance: “Encourage students to use AI as a tool, not a crutch… guide your students towards using ChatGPT as a starting point in research or a brainstorming partner-not a ghostwriter” (Carnegie Learning, 2023). This balanced approach acknowledges AI’s presence while maintaining academic standards.

Another example of algorithmic bias occurs when “an algorithm encodes the biases present in society, producing predictions or inferences that are clearly discriminatory towards specific groups” (Baker & Hawn, 2021). In educational contexts, this can manifest from biased student performance predictions to inequitable resource allocation.

Teachers can apply these insights by developing classroom-specific guidelines that clearly define appropriate AI use for different assignments. Creating tiered permissions-from restrictive (no AI) to permissive (AI allowed)-based on learning objectives helps students understand when and how AI tools can ethically support their learning while developing their independent thinking skills.

References

Atos. (2024). Is ChatGPT your biggest fan? Avoiding confirmation bias in AI. Retrieved from: https://atos.net/en/blog/is-chatgpt-your-biggest-fan-avoiding-confirmation-bias-biased-ai

Carnegie Learning. (2023). How to help students use AI ethically. Retrieved from: https://www.carnegielearning.com/blog/ethical-ai-chatgpt-students/

KPMG. (2024). Students using generative AI confess they’re not learning as much. Retrieved from: https://kpmg.com/ca/en/home/media/press-releases/2024/10/students-using-gen-ai-say-they-are-not-learning-as-much.html

Swindell, A., Farag, A., Greeley, L., & Verdone, B. (2024). Against Artificial Education: Towards an Ethical Framework for Generative Artificial Intelligence (AI) Use in Education. Retrieved from: https://olj.onlinelearningconsortium.org/index.php/olj/article/view/4438

Baker, R. S., & Hawn, A. (2023). Algorithmic bias: The state of the situation and policy recommendations. OECD. https://www.oecd.org/en/publications/oecd-digital-education-outlook-2023_c74f03de-en/full-report/algorithmic-bias-the-state-of-the-situation-and-policy-recommendations_a0b7cec1.html