| Course Subject: | Speech Language Pathology course in Communication Sciences and Disorders |
| Student Level: | Graduate |
| Number of Students: | 20-30 |
| Developed by: | Nia Johnson, EdD, CCC-SLP, Assistant Program Director; Assistant Clinical Professor, Communication Sciences and Disorders, Bouvé College of Health Sciences |
What the Students Did
Graduate students in Speech-Language Pathology (SLP) analyzed realistic clinical case vignettes exploring how generative AI intersects with privacy, accessibility, and scope of SLP practice. Working in small groups, students identified potential benefits and risks of AI use in clinical scenarios, examined AI-generated outputs using instructor-provided, de-identified prompts, and critiqued those outputs for accuracy, bias, and ethical appropriateness. Groups then shared findings in a whole-class debrief connecting student observations to professional standards and the ASHA Code of Ethics. Students completed the activity with an individual written reflection submitted to Canvas.
Purpose
The purpose of this activity was to build AI literacy specific to speech-language pathology practice, grounded in patient-centered care and ethical professional decision-making. Students strengthened their ability to think critically about AI-generated clinical recommendations rather than accept them uncritically, and practiced identifying how AI intersects with patient privacy, accessibility, health equity, and the professional responsibilities of SLPs. The activity was designed to prepare graduate SLP students to be informed, ethical consumers of and collaborators with AI tools — capable of evaluating outputs in light of legal and ethical obligations rather than adopting them passively.
Assessment
Student work was assessed on three dimensions: participation in small-group and whole-class discussion (evidence of preparation, relevance of contributions, engagement with peers); quality of ethical reasoning (ability to identify harms and benefits, reference to ethical principles including privacy, beneficence, nonmaleficence, and equity, and recognition of SLP professional responsibilities); and critical stance toward AI output (demonstrated willingness to question, not just accept, AI responses). The written reflection was rated with a brief rubric assessing clarity, depth of insight, and connection to course content and professional issues. The activity can be structured as graded or credit/no-credit depending on course design. At the activity level, the instructor gathered informal feedback through an anonymous exit ticket or quick poll asking students to rate whether the activity increased their understanding of how AI intersects with SLP practice.
Faculty Reflections
Students are already engaging with AI in various ways, but many have not had structured opportunities to critically discuss its ethical implications in a clinical context. The case-based format worked well because it grounded those conversations in realistic situations students could connect to future practice. Discussions were most energized around patient privacy, accessibility, cultural responsiveness, and clinician accountability, which reinforced the importance of framing AI literacy through a human-centered lens. Future iterations will expand the variety of clinical cases and add more opportunities for students to evaluate AI-generated outputs in real time, drafting vignettes that reflect the full range of SLP settings — school, medical, and telepractice — and represent diverse client populations. Pre-readings should stay concise, since the goal is orientation, not deep technical background, build in time for tech hiccups and varied student comfort levels, and make sure rubric criteria reward critical thinking about AI rather than enthusiasm for it.
Step-by-Step Directions
| Step 1 | Before Class: Complete the assigned pre-class readings or short video on AI in healthcare and/or education; basic privacy and data protection concepts (HIPAA/FERPA context); and accessibility and equity considerations. Bring notes and questions to class. |
| Step 2 | In Class – Part 1: Case Review (Small Groups). Join your assigned small group of 3–5 students. Your group will receive a clinical case vignette involving AI (e.g., AI-generated reports, AI-assisted AAC tools, automated screening tools). Discuss and document: what problem the clinician is trying to solve; where and how AI is being used or proposed; the potential benefits for the client and family; and the risks or concerns related to privacy, accessibility, bias, or scope of practice. |
| Step 3 | In Class – Part 2: Guided AI Interaction. Using instructor-provided, de-identified prompts, engage with the designated AI tool or review AI sample outputs provided by the instructor. Discuss what seems helpful or accurate, what seems problematic, biased, or incomplete, and whether you would use any part of this output in your clinical documentation or planning, and why or why not. Record 2–3 key takeaways about the limitations and responsibilities when using AI as an SLP. |
| Step 4 | In Class – Part 3: Whole-group Debrief. Each group shares their case scenario, one potential benefit of AI in the case, and one major concern along with how an ethical SLP should respond. Participate in the instructor-led discussion on professional obligations (ethics, supervision of technology, informed consent), intersections with accessibility and health equity, and how AI may change — but not replace — the SLP role. |
| Step 5 | After Class – Individual Reflection. Submit a 1–2 paragraph reflection to Canvas by the assigned due date, addressing: one way AI might support your future clinical practice; one boundary you would set to protect clients (privacy, equity, scope); and one question you still have about AI in SLP. |
Related Materials
- See Nia’s full lesson outline here: Lesson Outline: AI in the Clinic, Ethical Decision-Making for the Next-Gen SLP
Download PDF version of this assignment
Return to AI Gallery Home