Rethinking homework in the AI era: A shift to in-class collaborative problem solving

Abstract

Collaboration and teamwork are essential skills for success in STEM disciplines, yet many classroom practices still rely heavily on individual work that has to be completed outside of class. Recent advances in generative AI tools challenge this traditional approach by enabling students to generate solutions with minimal conceptual engagement, thereby undermining homework's efficacy while highlighting the collaboration skills it often neglects. This study examines the impact of transitioning from traditional individual homework to collaborative, in-class problem-solving as a more authentic approach to improving student learning and performance. Over two consecutive years, the same undergraduate Thermodynamics course was taught using two different pedagogical models. In the first year, students completed homework individually outside of class. In the second year, individual homework assignments were replaced with structured, in-class group work. In this model, students worked in small groups to solve problems and subsequently presented their solutions to the class. The instructor facilitated discussions by guiding problem-solving strategies, checking conceptual understanding, and encouraging student engagement. While both models covered identical content with comparable problem difficulty, students in the collaborative format demonstrated stronger problem-solving skills and achieved approximately 5–10% higher exam scores, along with an overall course grade increase of about 8%. Student feedback consistently reported improved conceptual understanding through peer discussion and greater confidence in applying thermodynamics principles independently. These findings suggest that replacing homework with guided, in-class collaboration creates a more engaging learning environment and better prepares students for the collaborative and problem-solving demands of modern engineering practice, particularly in the AI era.