Course Description
AI-driven clinical decision-making using behavioral and cognitive data. Students learn to interpret psychophysiological signals and behavioral patterns for diagnosis, treatment, and monitoring across pharmacy, medicine, veterinary medicine, and nursing through labs, case studies, and projects, gaining applied skills in personalized care, and interdisciplinary healthcare innovation.
Additional Requirements for Graduate Students:
Graduate students enrolled in this course are expected to demonstrate advanced engagement with course materials and contribute domain-specific expertise to interdisciplinary discussions. In addition to the standard course requirements, graduate students will:
Extended Literature Review
Submit a 1,000–1,500-word literature review (Week 4 assignment) that includes:
At least 5 peer-reviewed sources
A critical synthesis of how behavioral data is used in AI-driven diagnostics
Identification of gaps or future directions in the field
Graduate-Level Final Project Contribution
Lead or co-lead one aspect of the team’s final project
Submit an individual reflection (750–1,000 words) on the interdisciplinary integration of behavioral analytics in AI in their field, including ethical considerations and future applications
Athena Title
AI and Behavioral Analytics
Undergraduate Pre or Corequisite
BIOS 2010 or BIOS 2010E or STAT 2000 or STAT 2000E or VPHY(PHRM) 4350/6350 or permission of department
Semester Course Offered
Offered spring
Grading System
A - F (Traditional)
Student learning Outcomes
- Students will describe how behavioral and cognitive data can enhance AI models used in healthcare.
- Students will identify key AI techniques (e.g., machine learning, neural networks, NLP) and their relevance to behavioral and health analytics.
- Students will analyze psychophysiological and behavioral datasets to extract meaningful clinical insights.
- Students will evaluate the effectiveness of AI systems that incorporate behavioral data in improving patient outcomes.
- Students will compare interdisciplinary approaches to behavioral and health analytics in pharmacy, medicine, veterinary medicine, and nursing.
- Students will design and critique AI-driven tools that use behavioral data for clinical decision support.
- Students will apply ethical reasoning to the use of sensitive behavioral and cognitive data in AI systems.
Topical Outline
- Introduction to Behavioral AI Analytics in Healthcare: Introduces the foundational concepts of behavioral data and its relevance to AI-driven clinical decision-making. Students explore how human behavior, cognition, and psychophysiological signals can be modeled to improve healthcare outcomes.
- AI Concepts & Healthcare Modeling: Students learn core AI techniques such as machine learning and neural networks, with a focus on modeling healthcare scenarios. Emphasis is placed on recognizing behavioral patterns and translating them into actionable insights.
- Psychophysiological Data and AI: This topic examines how AI systems interpret physiological signals like heart rate, EEG, and eye tracking to assess patient states. Students analyze real-world datasets to understand how these signals inform diagnosis and monitoring.
- Cognitive, Affective, and Behavioral Markers in Clinical AI: Students explore how attention, memory, and executive function can be quantified and integrated into AI models. The module emphasizes the role of cognitive data in enhancing diagnostic precision and treatment personalization.
- Behavioral Analytics in Pharmacy: This topic focuses on using AI to model patient behavior related to medication adherence and pharmacy workflows. Students evaluate predictive tools that identify risk factors for non-compliance and optimize pharmaceutical care.
- Behavioral AI in Medicine: Students investigate how AI supports behavioral health and general medical screening to include chronic disease management. The module highlights applications in mental health, physical health, patient engagement, and personalized treatment planning.
- Veterinary Behavioral Data: This topic explores the use of AI in analyzing animal behavior, including herd dynamics and individual health indicators. Students discuss the translation of human behavioral models to veterinary contexts.
- Nursing and Behavioral Monitoring: Students examine how AI tools assist nurses in triage, workflow optimization, and patient engagement. Behavioral dashboards and monitoring systems are evaluated for their impact on care delivery.
- Data Integration & Bias: This topic addresses the challenges of combining multimodal behavioral data and detecting bias in datasets. Students learn techniques for ensuring fairness and accuracy in AI-driven healthcare analytics.
- Explainable AI & Human Factors: Students explore the importance of transparency and trust in clinician-AI interactions. The module emphasizes explainability as a key factor in behavioral AI adoption and ethical deployment.
- Ethics of Behavioral Data Use: This topic covers privacy, consent, and algorithmic fairness in the use of sensitive behavioral and cognitive data. Students develop policy briefs to guide ethical AI practices in their disciplines.
- AI Tools & Behavioral Platforms: Students review and compare platforms such as BioGPT, Affectiva, OpenFace, and CogStack. The focus is on evaluating tool capabilities for behavioral data analysis and clinical decision support.
Institutional Competencies Learning Outcomes
Analytical Thinking
The ability to reason, interpret, analyze, and solve problems from a wide array of authentic contexts.
