This conceptual paper aims to stimulate dialogue between industry and academia on the contrasting approaches to implementing AI guardrails in business and education. It emphasizes the need for AI guardrails in education to balance safety with exploration, fostering a transformative learning process.
Drawing from existing literature, this paper employs an exploratory approach, to uncover the divergent roles of AI guardrails in business and education. The study highlights the necessity of designing and implementing context-specific guardrails for education, addressing ethical considerations and promoting meaningful AI-driven learning outcomes.
The study identifies a critical divergence in AI guardrail implementation between business and education, highlighting the need for an ethical and philosophical framework. Addressing this paradox requires a balanced approach that integrates both exploration and regulation. In this regard, frameworks such as inverse reinforcement learning (IRL) and cooperative inverse reinforcement learning (CIRL) provide valuable mechanisms for aligning AI applications with ethical considerations in diverse contexts.
This study is based on existing literature, and further empirical research is needed to generate deeper insights and validate the proposed concepts.
Educators must rethink traditional assessment methods to address the challenges posed by AI. Implementing guardrails that encourage exploration and ethical reasoning will better equip students for AI-driven decision-making in professional settings. Policymakers must account for the distinct needs of business and education when formulating AI regulations.
While prior research has focused on AI guardrails in business, to our understanding, this paper is among the first to explore their implementation in educational contexts. It delineates the distinct approaches required for effective guardrails in both domains, advancing the discourse on responsible AI integration.
