Bias and representation in AI generated text-to-image in education: A systematic review

The rapid integration of generative AI into K–12 education presents both opportunities and challenges for supporting students' learning. This mixed-methods case study examined how high school students interacted with an AI-enhanced resume writing unit and a text-based mock interview unit within an English Language Arts course. Quantitative results indicated students' positive perceptions of AI utility across constructs of awareness, usage, evaluation, and ethics. Qualitative analysis revealed that students used AI to explore career information, generate and refine resume content, and iteratively revise and refine interview responses. While many students engaged critically—questioning, personalizing, and revising AI suggestions—others adopted a more passive or overly dependent approach. Students reported feeling more confident and valued individualized feedback, yet they also identified challenges such as AI's generic or decontextualized responses, difficulties interpreting AI feedback, and ethical concerns regarding authenticity, plagiarism, and overreliance. This study argues that meaningful integration of generative AI in secondary classrooms requires explicit instruction in prompting, verification, and critical evaluation so that students develop both the agency and the skills necessary to use AI as a scaffold rather than a substitute for learning.