Responsible digital innovation is not defined by adopting the newest technology. It begins with student learning needs, is guided by evidence, is strengthened through faculty collaboration, and uses artificial intelligence to augment—not replace—human expertise and judgment. This webinar presents a higher education case study in which learning analytics, instructional redesign, faculty collaboration, and human-in-the-loop AI were integrated to address performance and retention challenges in online Anatomy and Physiology courses. Participants will examine how the institution moved through a complete improvement cycle: identifying academic risk patterns, collaborating with faculty to interpret the findings, redesigning course and remediation processes, developing a faculty-facing AI assistant, evaluating results, and scaling effective practices into a second course.
The case demonstrates how AI can reduce repetitive faculty workload and support more timely, consistent, and individualized remediation while preserving faculty judgment, accountability, and oversight. Participants will also explore strategies for engaging faculty as partners in innovation, designing AI-supported workflows around clearly defined instructional problems, and maintaining human review of AI-generated analyses and resources. This webinar will benefit professionals seeking a realistic, scalable approach to leveraging existing LMS data and emerging AI tools to improve student outcomes and address faculty workload challenges. Attendees will leave with a practical model for connecting course-level analytics, faculty development, and responsible AI adoption to student success and institutional continuous improvement.
Intended Audience
Digital learning leaders, instructional designers, learning analytics professionals, faculty, and others responsible for improving student success.
Key Messages and Takeaways
- Apply a problem-first approach to digital innovation by using LMS data to identify actionable academic risk patterns and instructional priorities.
- Translate learning analytics into a coordinated student-success framework that supports readiness, early intervention, and targeted remediation.
- Design human-in-the-loop AI workflows in which faculty retain responsibility for instructional decisions.
- Build faculty adoption through collaboration, training, pilot testing, feedback, and iterative refinement.
- Connect course-level improvement initiatives to institutional priorities such as retention, instructional consistency, responsible AI adoption, academic quality, and continuous improvement.
Presenter
Lana Mett| Director of Curriculum, Instruction, and Assessments, FVI School of Nursing and Technology
Dr. Lana Mett has over 8 years of experience in higher education and a strong passion for shaping the future of education through pedagogical innovation and online learning technologies. She is currently the Director of Curriculum, Instruction, and Assessment at FVI School of Nursing and Technology, where she collaborates with subject matter specialists to design, develop, and evaluate online curricula. In this role, she focuses on delivering quality, competency-based educational experiences by integrating Quality Matters standards, Universal Design for Learning (UDL) principles, and artificial intelligence into course design. Prior to her current role, Lana served as a curriculum developer and instructor at the University of West Florida. Lana holds a Doctor of Education (EdD) in Curriculum and Instruction from the University of West Florida. Dedicated to continuous professional growth within digital learning, she also holds a Digital Education Leader (DEL) Certification through the Online Learning Consortium.