Introduction to AI Tools in Accreditation

• In Event: Artificial Intelligence as a Catalyst for Transforming Accreditation in Educator Preparation

Abstract

As educator preparation programs (EPPs) face increasing demands for evidence-based reporting, accountability, and improvement, artificial intelligence (AI) offers powerful tools to support and streamline the accreditation process. This session provides a practical introduction to the types of AI applications currently being adopted in accreditation contexts, highlighting how these tools can enhance accuracy, efficiency, and strategic decision-making across the accreditation cycle.

The presentation begins by defining key AI technologies—including natural language processing (NLP), machine learning (ML), and large language models (LLMs)—in plain terms, demystifying their relevance to educator preparation. Real-world examples will demonstrate how EPPs use AI to tag and align evidence to accreditation standards (such as CAEP or state-specific frameworks), organize documentation, automate report writing, and create interactive dashboards that visualize performance trends across candidate data.

Participants will also explore AI’s potential in improving preparation for site visits. From generating self-study narrative drafts to simulating evaluation scenarios and flagging gaps in evidence, AI tools are increasingly becoming part of the institutional toolkit for accreditation preparation and management. The presenter will address practical considerations for selecting, deploying, and maintaining these tools, including collaboration with IT teams, cross-functional training, and integration with institutional data systems.

In the second half of the session, the presenter transitions into the role of discussant—facilitating audience interaction and reflection on AI tool adoption in diverse institutional contexts. Participants will be invited to share their experiences with using AI in evidence management, reporting, and assessment alignment. Common implementation challenges—such as data silos, change resistance, and limited AI literacy—will be addressed through group dialogue and shared problem-solving.

Attendees will leave with a toolkit of actionable strategies to begin—or refine—the use of AI in their own accreditation workflows, along with guiding questions to assess institutional readiness and alignment with ethical and quality assurance goals. Key takeaways include:

A practical overview of current AI tools relevant to accreditation.

Examples of how AI supports data organization, evidence alignment, and reporting.

Opportunities and challenges of AI adoption in educator preparation.

A peer-informed discussion on real-world implementation and lessons learned.

This session is ideal for faculty, accreditation coordinators, assessment professionals, and institutional researchers who are exploring the integration of AI into their accreditation processes or seeking to deepen their current practice with these technologies.

Author

• Anne Renee Tapp Jaksa, Saginaw Valley State University