Balancing AI-assisted learning and traditional assessment: the FACT assessment in environmental data science education
Open accessAbstract
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.
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Multi-source assessment ecosystems combining self-assessment, peer evaluation, AI analytics, and instructor judgment provide triangulated evidence of competence.
“Multi-source assessment ecosystems combining self-assessment, peer evaluation, AI analytics, and instructor judgment provide triangulated evidence of competence (Elshall and Badir, 2025; Francis et al., 2025; Tang et al., 2024; Zhang and Zhang, 2022).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Elshall and Badir, 2025
Section 2.1 describes FACT as a multicomponent framework for comprehensive assessment, and section 4.5.2 says instructors can use its AI-free and AI-assisted tasks to monitor students’ independent skills. The paper does not describe an assessment ecosystem combining self-assessment, peer evaluation, AI analytics, and instructor judgment, or claim that those sources provide triangulated evidence of competence. (LLM, conf=0.85)
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- Authors
- Elshall, A. S., & Badir, A.
- Affiliations
- Florida Gulf Coast University (US)
- Corresponding author
- Elshall, Ahmed S.
- Venue
- Frontiers in Education
- Publisher
- Frontiers Media SA
- Published
- 2025-06-13
- Volume
- 10
- ISSN
- 2504-284X (ISSN-L)
- OpenAlex
- W4411292450
- Cited by
- 28
- References
- 60
- Citations by year
- 2026: 21 · 2025: 7
- FWCI
- 29.36
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Education (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Keywords
- AI-assisted learning, assessment design, ChatGPT, overreliance on AI, critical thinking assessment, student perceptions of AI tools, geoscience education
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