As AI technologies become more prevalent in education and clinical training, Communication Sciences and Disorders (CSD) programs must develop institutional guidelines, policies, and frameworks to ensure responsible and effective AI integration. This section provides guidance for programs and institutions on AI adoption, including policy templates, risk assessment strategies, accessibility considerations, and accreditation implications.
Sample AI Policy Templates for Curriculum Integration
Programs should establish clear and consistent policies on AI use in coursework, clinical training, and research. Below are key components of an AI policy:
Scope of AI Use
- Define where AI is permitted, encouraged, or restricted (e.g., coursework, clinical documentation, research).
- Differentiate between acceptable AI-assisted learning (e.g., brainstorming, summarization) and prohibited uses (e.g., submitting AI-generated clinical reports without verification).
Academic Integrity & Disclosure
- Require explicit disclosure of AI use in assignments and research.
- Provide examples of appropriate citation formats for AI-generated content.
Guidelines for Faculty & Supervisors
- Encourage faculty to model appropriate AI use in teaching, feedback, and assessment.
- Offer professional development on AI best practices to ensure informed integration.
Assessment & Evaluation Policies
- Adapt grading rubrics to differentiate AI-assisted work from original student contributions.
- Consider alternative assessments (e.g., oral defenses, critical reflections) to ensure genuine understanding.
Ethical & Privacy Considerations
- Address confidentiality by prohibiting the use of real patient/client data in AI systems.
- Require faculty and students to verify AI-generated clinical information before use.
Programs can use these policy components to develop their own institutional guidelines or adapt existing academic integrity policies to include AI.
Risk Assessment Frameworks for AI Adoption in Clinical Settings
Before implementing AI tools in clinical training and supervision, programs should conduct risk assessments to identify potential challenges and mitigation strategies.
Clinical Accuracy & Reliability
- How will AI-generated treatment plans, SOAP notes, or assessments be verified for accuracy and adherence to best practices?
- Will faculty review and approve AI-generated clinical documentation before student submission?
Confidentiality & Compliance
- Are AI tools HIPAA-compliant, and do they align with privacy regulations?
- How will students and supervisors be trained to avoid entering sensitive patient data into AI systems?
Bias & Equity in AI-Generated Content
- Does the AI system demonstrate bias in case studies, diagnostic suggestions, or intervention recommendations?
- How will faculty and students critically evaluate AI-generated materials for fairness and inclusivity?
Supervisory Oversight & Accountability
- Will there be faculty-supervised AI use in clinical documentation and decision-making?
- How will institutions track and assess AI’s impact on clinical training outcomes?
A structured risk assessment process ensures that AI adoption in clinical settings enhances learning while maintaining ethical and legal compliance.
Accessibility Considerations to Ensure Inclusive AI Use
AI can enhance accessibility in CSD programs, but institutions must ensure equitable AI adoption by addressing potential barriers.
AI as an Accessibility Tool
- Text-to-speech (TTS) and speech-to-text (STT) AI tools can support students with disabilities (e.g., dyslexia, hearing impairments).
- AI-powered real-time captioning can improve lecture accessibility for diverse learners.
Mitigating Bias & Language Limitations
- AI-generated materials must be evaluated for bias against non-standard dialects, multilingual populations, and underrepresented communities.
- Institutions should train students and faculty to recognize linguistic biases in AI-generated transcriptions or case scenarios.
Ensuring Equal Access to AI Tools
- Programs should offer AI training sessions to ensure all students, including those with disabilities, can effectively use AI technologies.
- AI tools should be made available through institutional licenses or accessibility grants for students with financial constraints.
By prioritizing accessibility, institutions ensure that AI serves as a tool for inclusion rather than a barrier to learning.
Accreditation & Licensure Implications for AI-Assisted Documentation and Training
AI’s role in clinical documentation, assessment, and training raises important considerations for accreditation, certification, and licensure requirements.
CAA Accreditation & AI Use
- AI-assisted learning must align with Council on Academic Accreditation (CAA) standards for clinical education and assessment.
- Programs should ensure AI use does not replace hands-on clinical experience required for accreditation.
CFCC & ASHA Certification Considerations
- AI-generated clinical documentation should not replace a student’s ability to independently write case notes, SOAP notes, and treatment plans.
- Students using AI in clinical assignments must demonstrate competency in manual documentation, ethical reasoning, and decision-making.
State Licensure & AI in Clinical Practice
- State boards may have varying policies on AI-generated documentation—programs should review and align training with licensure requirements.
- AI use should be explicitly addressed in program guidelines to ensure compliance with state and national clinical competency expectations.
By staying informed about accreditation and licensure policies, institutions can ensure that AI integration supports competency-based education while maintaining professional standards.
Final Thoughts
AI presents both opportunities and challenges for CSD programs. By implementing clear policies, risk assessments, accessibility considerations, and compliance strategies, institutions can responsibly integrate AI while preparing students for the evolving role of AI in speech-language pathology and audiology.
Updated November 2025