Balancing AI-assisted learning and traditional assessment: the FACT assessment in environmental data science education - Elshall, Ahmed S.; Badir, Ashraf (Frontiers in Education, 2025)
As artificial intelligence (AI) tools evolve, a growing challenge faced by educators is how to leverage the invaluable AI-assisted learning, while maintaining rigorous assessment. AI tools, such as ChatGPT and Jupyter AI coding assistant, enable students to tackle advanced tasks and real-world applications. However, they also risk overreliance, which can diminish cognitive and skill development, and complicate assessment design. To address these challenges, the Fundamental, Applied, Conceptual, critical Thinking (FACT) assessment was implemented in an Environmental Data Science course for upper-level undergraduate and graduate students from civil and environmental engineering, and Earth sciences. By balancing traditional and AI-based assessments, the FACT assessment includes: (1) Fundamental skills assessment (F) through assignments without AI assistance to build a strong coding foundation, (2) applied project assessment (A) through AI-assisted assignments and term projects to engage students in authentic tasks, (3) conceptual-understanding assessment (C) through a traditional paper-based exam to independently evaluate comprehension, and (4) critical-thinking assessment (T) through complex multi-step case study using AI, to assess critical problem-solving skills. Analysis of student performance shows that both AI tools and AI guidance improved student performance and allowed them to tackle complex tasks and real-world applications versus AI tools alone without guidance. Survey results show that many students found AI tools beneficial for problem solving, yet some students expressed concerns about overreliance. By integrating assessments with and without AI tools, FACT assessment promotes AI-assisted learning while maintaining rigorous academic assessment to prepare students for their future careers in the AI era.