Over the past year, the rise of generative AI in college classrooms has sparked significant discussions around academic integrity, detection strategies, and grading effectiveness. However, it has unveiled deeper issues regarding student comprehension. As a computer science faculty member, I noticed that while students produced functioning code, many struggled to explain their solutions, revealing a disconnect between output and understanding. This trend isn’t unique to computer science; it extends across engineering, data science, and writing courses, where AI quickly generates solutions but does not foster critical thinking.
To address this, I shifted my assessment strategies: requiring explanations for solutions, predictions before execution, and comparisons of various approaches, including AI-generated code. This approach emphasizes reasoning over mere reproduction, compelling students to demonstrate true understanding. Ultimately, AI serves not just as a challenge but as a catalyst, urging educators to reassess their focus on learning outcomes and deepen the intellectual engagement of students.
Source link
