MARC

LEADER 00000nab a2200000uu 4500
001 3170873298
003 UK-CbPIL
022 |a 2227-7102 
022 |a 2076-3344 
024 7 |a 10.3390/educsci15020156  |2 doi 
035 |a 3170873298 
045 2 |b d20250101  |b d20251231 
084 |a 231457  |2 nlm 
100 1 |a Diyab, Ayman  |u Faculty of Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada; <email>rmfrost@lakeheadu.ca</email> 
245 1 |a Engineered Prompts in ChatGPT for Educational Assessment in Software Engineering and Computer Science 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a AI Assess, a ChatGPT-based assessment system utilizing the ChatGPT platform by OpenAI, composed of four components, is proposed herein. The components are tested on the GPT model to determine to what extent they can grade various exam questions based on learning outcomes, generate relevant practice problems to improve content retention, identify student knowledge gaps, and provide instantaneous feedback to students. The assessment system has been explored using software engineering and computer science courses and is successful through testing and evaluation. AI Assess has demonstrated the ability to generate practice problems based on syllabus information and learning outcomes. The components have been shown to identify weak areas for students. Finally, it has been shown to provide different levels of feedback. The combined set of components, if incorporated into a complete software system and implemented in classrooms with proposed transparency mechanisms, has vast potential to reduce instructor workload, improve student understanding, and enhance the learning experience. The potential for GPT-powered chatbots in educational assessments is vast and must be embraced by the education sector. 
610 4 |a OpenAI 
653 |a Teaching 
653 |a Language 
653 |a Software 
653 |a Students 
653 |a Feedback 
653 |a Educational evaluation 
653 |a Educational technology 
653 |a English as a second language 
653 |a Education 
653 |a Computer science 
653 |a Chatbots 
653 |a Innovations 
653 |a Machine learning 
653 |a Human-computer interaction 
653 |a Artificial intelligence 
653 |a Educational objectives 
653 |a Learning outcomes 
653 |a Neural networks 
653 |a Engineering 
653 |a Natural language processing 
653 |a Large language models 
653 |a Transparency 
653 |a Learning 
653 |a Components 
653 |a Evaluation 
653 |a Classrooms 
653 |a Influence of Technology 
653 |a Teaching Methods 
653 |a Prompting 
653 |a Learning Processes 
653 |a Grading 
653 |a English (Second Language) 
653 |a Second Languages 
653 |a Student Improvement 
653 |a Knowledge Representation 
653 |a Brain 
653 |a Short Term Memory 
653 |a Feedback (Response) 
653 |a Educational Assessment 
653 |a Symbolic Learning 
653 |a Outcomes of Education 
653 |a Language Processing 
653 |a Engineering Education 
653 |a Networks 
653 |a Educational Facilities Improvement 
700 1 |a Russell Morris Frost  |u Faculty of Engineering, Lakehead University, Thunder Bay, ON P7B 5E1, Canada; <email>rmfrost@lakeheadu.ca</email> 
700 1 |a Fedoruk, Benjamin David  |u Mitch and Leslie Frazer Faculty of Education, Ontario Tech University, Oshawa, ON L1G 0C5, Canada 
700 1 |a Diyab, Ahmad  |u Shad Alumni, Western University, London, ON N6A 3K7, Canada; <email>ahmaddiyab0405@lakeheadschools.ca</email> 
773 0 |t Education Sciences  |g vol. 15, no. 2 (2025), p. 156 
786 0 |d ProQuest  |t Education Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3170873298/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text + Graphics  |u https://www.proquest.com/docview/3170873298/fulltextwithgraphics/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3170873298/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch