2026 ASEE Annual Conference & Exposition

This complete evidence-based paper presents several innovative approaches for first-year learners in an introduction to programming courses. As artificial intelligence (AI) becomes increasingly integrated into everyday technology, the way introductory programming is taught needs to evolve to prepare students for these new realities. This work closely examines a first-year introduction to programming course at a large Midwestern University as a case study. The course implemented several key methods to adapt instructions in the AI era by fostering student engagement, accessibility, and mastery. A central element of the course design is a semi-flipped classroom model. Core instructional content is delivered through “bite-sized” pre-recorded videos and readings, while in-class time is devoted to active learning, including hands-on project support and collaborative problem-solving. The PrairieLearn platform is employed to facilitate both pre-class preparation and in-person office hour/project help, offering students interactive and adaptive practice opportunities. Interactive presentation tools and online platforms, such as live polls and engaging sessions, are incorporated during class sessions to promote engagement, gauge student understanding, and encourage participation in real time. Additionally, frequent, low-stakes, mastery-based assessments, such as traditional pencil-and-paper quizzes, are integrated and complemented by digital platforms to provide real-time feedback and track student progress. To further boost motivation and contextualize learning, the course integrates hands-on robotics applications. These projects allow students to directly apply programming and algorithmic concepts to real-world robotic concepts and challenges, which provides a strong motivation for students to participate in the in-person learning experiences. Throughout the course, digital course materials and activities are designed to be accessible to all students, with the hope that students of varying backgrounds and abilities can fully participate and benefit from the curriculum. Through analysis of student feedback, performance data, and engagement metrics, we find these approaches show effectiveness in fostering understanding, mastery, and enthusiasm for programming, as demonstrated by high class performance and project completion and positive qualitative student feedback. We hope this work provides insights and practical recommendations for educators seeking to adapt introductory computer science education to the demands and opportunities presented by advances in AI, ultimately aiming to better equip first-year learners for future study and careers in computational and multidisciplinary engineering fields.