Identifying High-Confidence Social Biases in LLMs for Trustworthy Conversational Tutoring Agents
arXiv:2606.01584v1 Announce Type: new Abstract: Conversational tutoring agents have been shown to improve learning engagement and student outcomes, and large language models (LLMs) are increasingly used in these systems to provide scalable, personalized feedback. However, LLMs may perpetuate or amplify stereotypical social biases, posing particular risks in educational settings. In this study, we evaluate LLMs in conversational tutoring scenarios to identify high-confidence social biases, instances where models are unable to identify biased judgments in tutoring conversations while maintaining
As LLMs become ubiquitous in critical applications like education, the ethical implications of their inherent biases are becoming a pressing concern, necessitating immediate research and mitigation strategies.
Biases in AI tutoring agents can exacerbate inequalities, erode trust in educational technology, and lead to potentially harmful learning outcomes, impacting widespread adoption and efficacy.
This research highlights the urgent need for developers and deployers of AI tutors to actively identify and mitigate social biases, shifting focus beyond purely performance metrics to include ethical robustness.
- · AI ethics researchers
- · Educational technology developers focusing on bias mitigation
- · Students receiving unbiased AI-powered education
- · Unregulated LLM developers
- · Providers of biased AI tutoring systems
- · Educational institutions adopting unvetted AI
Increased scrutiny and demand for bias-aware LLMs in educational and other sensitive applications.
Development of new industry standards and regulatory frameworks for ethical AI deployment, especially in public-facing roles.
A shift in pedagogical approaches, incorporating AI literacy and critical evaluation of AI-generated content for both educators and students.
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