Predictive capability of foundational concepts tests for problem-solving using machine learning concepts: Evaluating project-based learning courses in artificial intelligence literacy education

In the artificial intelligence (AI) era, secondary and university students should be able to apply AI for problem-solving. This study designed and evaluated an AI literacy programme to enhance understanding of machine learning concepts. It also examined how the conceptual understanding from two foundational courses (Courses 1 and 2) affected students' application of these concepts in the subsequent two project-based learning courses (Courses 3 and 4). The regression analysis of data from 566, 566, 470, and 196 student participants enrolled on Courses 1, 2, 3, and 4, respectively, revealed that the post-course concept tests for Courses 1 and 2 accounted for 19.9 % of the variance in the students' problem-solving ability test before they took Course 3. This result indicates that teaching students' foundational concepts can develop their ability to solve machine learning-related problems. The post-course concept tests for Courses 1 and 2, together with the pre-course problem-solving ability test for Course 3, collectively explained 27.4 % of the variance in the students’ problem-solving ability after completing Course 3. Together with the significant improvement in the paired-samples t -test statistics for the pre- and post-course problem-solving test of Course 3, this indicates the importance of providing opportunities for students to solve machine learning-related problems. These findings provide empirical evidence to inform the design of curricula for AI literacy programmes. Project-based learning (PBL) is an approach that can provide opportunities for participants to develop problem-solving skills using foundational AI knowledge. • We evaluated the impact of conceptual understanding of machine learning on PBL courses for solving related problems. • Foundational machine learning concepts can develop students' ability to solve machine learning-related problems. • It is equally important to provide opportunities for students to use these concepts for problem-solving. • Project-based learning provides opportunities for students to use their concepts for solving problems. • AI literacy programmes should include both foundational concepts and opportunities for using them for problem-solving.

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Publication Details

Journal
Computers and Education Artificial Intelligence
Published
2025-11-20
DOI
https://doi.org/10.1016/j.caeai.2025.100503
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
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article

Predictive capability of foundational concepts tests for problem-solving using machine learning concepts: Evaluating project-based learning courses in artificial intelligence literacy education

Siu Cheung Kong, Chunyu Hou
Computers and Education Artificial Intelligence
Teaching and Learning Programming
article

Predictive capability of foundational concepts tests for problem-solving using machine learning concepts: Evaluating project-based learning courses in artificial intelligence literacy education

Siu Cheung Kong, Chunyu Hou
article en

Abstract

In the artificial intelligence (AI) era, secondary and university students should be able to apply AI for problem-solving. This study designed and evaluated an AI literacy programme to enhance understanding of machine learning concepts. It also examined how the conceptual understanding from two foundational courses (Courses 1 and 2) affected students' application of these concepts in the subsequent two project-based learning courses (Courses 3 and 4). The regression analysis of data from 566, 566, 470, and 196 student participants enrolled on Courses 1, 2, 3, and 4, respectively, revealed that the post-course concept tests for Courses 1 and 2 accounted for 19.9 % of the variance in the students' problem-solving ability test before they took Course 3. This result indicates that teaching students' foundational concepts can develop their ability to solve machine learning-related problems. The post-course concept tests for Courses 1 and 2, together with the pre-course problem-solving ability test for Course 3, collectively explained 27.4 % of the variance in the students’ problem-solving ability after completing Course 3. Together with the significant improvement in the paired-samples t -test statistics for the pre- and post-course problem-solving test of Course 3, this indicates the importance of providing opportunities for students to solve machine learning-related problems. These findings provide empirical evidence to inform the design of curricula for AI literacy programmes. Project-based learning (PBL) is an approach that can provide opportunities for participants to develop problem-solving skills using foundational AI knowledge. • We evaluated the impact of conceptual understanding of machine learning on PBL courses for solving related problems. • Foundational machine learning concepts can develop students' ability to solve machine learning-related problems. • It is equally important to provide opportunities for students to use these concepts for problem-solving. • Project-based learning provides opportunities for students to use their concepts for solving problems. • AI literacy programmes should include both foundational concepts and opportunities for using them for problem-solving.

Computers and Education Artificial IntelligenceVol. 9
Education University of Hong Kong (HK)
Openalex Percentile: Top 62%
Teaching and Learning Programming
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