As generative AI becomes more integrated into Communication Sciences and Disorders (CSD) education, clinical training, and research, ethical considerations must be at the forefront of its use. Ensuring that AI-generated content is accurate, equitable, and responsibly implemented is critical to maintaining professional and academic integrity. Below are key ethical concerns and best practices for AI use in CSD programs.
Accuracy: Verifying AI-Generated Content for Clinical and Educational Reliability
AI-generated content should be treated as a starting point, not a final authority in clinical and educational contexts. AI systems are trained on vast datasets but do not have inherent expertise or judgment.
Verify all AI-generated case studies, learning materials, and clinical recommendations against peer-reviewed sources and professional guidelines (e.g., ASHA practice policies).
Ensure AI-generated explanations align with evidence-based practices before incorporating them into coursework or supervision.
Encourage critical evaluation—students and educators should analyze AI-generated information for potential inaccuracies or inconsistencies.
Example: A student uses AI to generate a sample treatment plan for a child with speech sound disorders. Before submitting it, they cross-check the recommendations with ASHA’s guidelines and research articles to ensure clinical validity.
Privacy & Confidentiality: Ensuring HIPAA Compliance When Using AI for Case Discussions
Generative AI tools do not inherently comply with HIPAA regulations, and inputting protected health information (PHI) into AI platforms risks confidentiality violations.
Avoid entering patient names, birthdates, or identifiable clinical details into AI systems.
Use de-identified or fictionalized case information when discussing clinical examples with AI.
Institutions should establish clear guidelines on AI use in clinical documentation and supervision to prevent accidental data breaches.
Example: A clinical educator asks an AI tool to generate a SOAP note. Instead of providing real patient data, they use a de-identified case with general details to protect confidentiality.
Attribution: Guidelines for Properly Citing AI-Assisted Work
Just as students and researchers cite traditional sources, proper attribution of AI-generated content is essential for academic integrity and transparency.
Follow institutional guidelines on AI citation (e.g., APA, MLA, or journal-specific policies).
Clearly disclose AI assistance in academic and clinical work where applicable.
Avoid presenting AI-generated content as entirely original work—students should critically engage with and refine AI outputs.
Example: A student uses AI to help structure a research paper’s introduction. They add a note in their methodology section stating, "ChatGPT was used to generate an initial draft, which was then revised and expanded upon by the author."
Equity & Bias: Addressing Limitations and Biases in AI-Generated Content
AI models are trained on large datasets that may reflect societal biases in language, healthcare access, and cultural perspectives. This can lead to biased outputs that disproportionately misrepresent certain populations.
Critically evaluate AI-generated content for bias, especially in case studies, treatment recommendations, and language-based outputs.
Use diverse, inclusive examples in AI-generated teaching materials to represent various linguistic, cultural, and socioeconomic backgrounds.
Encourage discussions on AI bias and its impact on clinical decision-making and research in CSD.
Example: An AI tool generates a case study featuring only English-speaking clients. The instructor modifies the case to include bilingual considerations, ensuring students engage with diverse linguistic profiles.
Academic Integrity: Establishing Clear Policies on AI Usage in Coursework and Research
AI can be a valuable tool for learning and research, but institutions must establish clear boundaries to prevent misuse.
Define acceptable and unacceptable AI uses in coursework, clinical assignments, and research.
Develop AI policies for syllabi, stating whether AI can be used for brainstorming, outlining, or content generation.
Encourage faculty to design assessments that require critical thinking and original student input, reducing over-reliance on AI-generated content.
Example: A university policy states that AI may be used for idea generation but not for final assignment submissions unless explicitly permitted by the instructor. Faculty members provide guidance on proper AI usage in their courses.
AI Detectors: Pros and Cons
The emergence of AI-generated content has led to the development of AI detection tools, designed to identify text or other media potentially created by artificial intelligence. It is crucial for CSD programs to understand the capabilities and limitations of these tools.
Pros:
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Potential Deterrent: AI detectors might discourage students from solely relying on AI for assignments, promoting more thoughtful engagement with the material.
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Identification of Potential Plagiarism: In some cases, detectors could flag submissions that warrant further review for potential academic dishonesty.
Cons:
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Inaccuracy and Unreliability: AI detectors are far from perfect. They can produce both false positives (flagging human-created content as AI-generated) and false negatives (failing to detect AI-generated content). These inaccuracies make them unsuitable for high-stakes decisions regarding academic integrity.
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Bias and Discrimination: Like many AI systems, detectors may exhibit biases against certain writing styles, potentially penalizing students from diverse backgrounds, highly structured writing styles, or those with different linguistic styles.
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Circumvention: Students determined to use AI can often find ways to circumvent detection, rendering the tools ineffective. This creates a "cat and mouse" game that distracts from genuine learning and ethical development.
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Focus on Detection, Not Learning: Over-reliance on AI detection tools shifts the focus from fostering genuine learning and critical thinking to simply catching students using AI. This undermines the educational goals of CSD programs.
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Ethical Concerns: The use of AI detection tools raises ethical questions about privacy and surveillance.
Discouragement of Standalone Use:
CSD programs are discouraged from using AI detection tools as the sole or primary means of determining academic honesty. The inherent limitations and potential biases of these tools make them unreliable for this purpose. Instead, programs should prioritize educational strategies that promote academic integrity, such as:
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Clear Expectations: Clearly articulate policies regarding AI use in coursework and clinical training.
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Meaningful Assessments: Design assessments that require critical thinking, problem-solving, and original thought, making it more difficult to rely solely on AI.
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Emphasis on the Learning Process: Focus on the learning process rather than just the final product. Encourage drafts, revisions, and discussions that promote deeper understanding.
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Open Dialogue: Foster open discussions about the ethical implications of AI in CSD and the importance of academic integrity.
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Faculty Development: Provide faculty with training on how to design effective assignments, assess student learning, and address potential issues related to AI use.
By focusing on these strategies, CSD programs can create a learning environment that encourages responsible AI use and promotes genuine academic integrity, rather than relying on flawed detection tools. AI should be viewed as a tool to enhance learning, not a shortcut to bypass it.
Moving Forward: Ethical AI Use in CSD
By proactively addressing these ethical concerns, CSD programs can harness the benefits of AI while upholding professional, clinical, and academic standards. Faculty, students, and clinicians should be encouraged to use AI thoughtfully, transparently, and responsibly to enhance learning, clinical practice, and research in the field.
Updated November 2025