As a chemistry educator with nearly two decades of college teaching experience, the rapid advancements in Artificial Intelligence (AI) and its implications for academic integrity are at the forefront of my mind, especially as I navigate new faculty applications and interviews. A recent publication, “Artificial Intelligence and Academic Integrity: Legislate or Educate?” by Wan et al. (2025) in the Journal of Scholarly Publishing, offers timely insights into this critical discussion.

The study, a participatory action research project conducted at a Midwestern university, dives into how stakeholders—faculty, staff, and students—perceive and use Generative AI (GenAI) tools while striving to uphold academic integrity. Using a mixed-methods approach, the researchers aimed to bridge the gap between emerging GenAI tools and established academic integrity principles.

Key Findings:

The research highlights a significant sentiment among stakeholders: a widespread disappointment with the existing support and guidance for integrating GenAI tools. This isn’t due to the tools themselves, but rather the perceived lack of institutional support, adequate user training, and allocation of technological resources. As one faculty member noted, there’s a distinct “lack of cohesion” across academic units in addressing AI.

The study revealed a pressing need for a “concerted call for cohesive efforts toward constructive AI education and integration”. This means fostering an educational ecosystem where AI isn’t just taught theoretically but is actively integrated into practical experiences, promoting a hands-on learning approach. A crucial emerging theme emphasizes education over legislation, suggesting that understanding and “demystifying” these tools is key to effective use.

Impact on Different Stakeholders:

  • Faculty and Staff: While there was general agreement on the need for AI resources and institutional support, faculty ratings on “clear and up-to-date academic integrity guidelines” showed significant variation across different academic units. This indicates a diverse range of attitudes, from mild agreement to strong disagreement, with current policies.
  • Students: Student responses about institutional AI support and guidelines tended towards disagreement or neutrality, with scores consistently below 2. Interestingly, students’ personal AI strategies were not significantly influenced by whether institutional guidelines or training existed for academic integrity in AI usage.

A Path Forward: The CAIL Framework

In response to these challenges, the authors propose a Critical Artificial Intelligence Literacy (CAIL) framework. This framework encourages the development of essential skills for ethical GenAI navigation, including:

  • Access: Knowing what GenAI tools exist, where to find them, and how to use them responsibly.
  • Analysis: Identifying developers, purposes, and inherent biases within GenAI tools and their outputs.
  • Evaluation: Recognizing the broader social and political implications of GenAI, such as environmental impacts and data poverty.
  • Creation: Applying GenAI tools effectively, ethically, and critically to create new content.
  • Action: Reflecting on one’s conduct and applying social responsibility to create a critical AI-literate environment.

The research firmly advocates for a human-centered approach to AI policies, guidelines, and education, rather than outright bans. This approach aims to equip all stakeholders to leverage AI’s transformative potential while maintaining academic integrity.

For us educators in Chicago Public Schools and beyond, this study underscores the urgent need for proactive engagement with AI. It’s not about ignoring or simply restricting these powerful tools, but rather about understanding them, developing critical literacy, and adapting our pedagogical approaches to foster responsible and ethical use. This research provides a valuable roadmap for institutions to navigate the complex, yet opportunity-rich, landscape of AI in education.