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Po-Chun Huang; Ying-Hong Chan; Ching-Yu Yang; Hung-Yuan Chen; Yao-Chung Fan – IEEE Transactions on Learning Technologies, 2024
Question generation (QG) task plays a crucial role in adaptive learning. While significant QG performance advancements are reported, the existing QG studies are still far from practical usage. One point that needs strengthening is to consider the generation of question group, which remains untouched. For forming a question group, intrafactors…
Descriptors: Automation, Test Items, Computer Assisted Testing, Test Construction
A Method for Generating Course Test Questions Based on Natural Language Processing and Deep Learning
Hei-Chia Wang; Yu-Hung Chiang; I-Fan Chen – Education and Information Technologies, 2024
Assessment is viewed as an important means to understand learners' performance in the learning process. A good assessment method is based on high-quality examination questions. However, generating high-quality examination questions manually by teachers is a time-consuming task, and it is not easy for students to obtain question banks. To solve…
Descriptors: Natural Language Processing, Test Construction, Test Items, Models
Samah AlKhuzaey; Floriana Grasso; Terry R. Payne; Valentina Tamma – International Journal of Artificial Intelligence in Education, 2024
Designing and constructing pedagogical tests that contain items (i.e. questions) which measure various types of skills for different levels of students equitably is a challenging task. Teachers and item writers alike need to ensure that the quality of assessment materials is consistent, if student evaluations are to be objective and effective.…
Descriptors: Test Items, Test Construction, Difficulty Level, Prediction
Semere Kiros Bitew; Amir Hadifar; Lucas Sterckx; Johannes Deleu; Chris Develder; Thomas Demeester – IEEE Transactions on Learning Technologies, 2024
Multiple-choice questions (MCQs) are widely used in digital learning systems, as they allow for automating the assessment process. However, owing to the increased digital literacy of students and the advent of social media platforms, MCQ tests are widely shared online, and teachers are continuously challenged to create new questions, which is an…
Descriptors: Multiple Choice Tests, Computer Assisted Testing, Test Construction, Test Items
Mohammad Hmoud; Hadeel Swaity; Eman Anjass; Eva María Aguaded-Ramírez – Electronic Journal of e-Learning, 2024
This research aimed to develop and validate a rubric to assess Artificial Intelligence (AI) chatbots' effectiveness in accomplishing tasks, particularly within educational contexts. Given the rapidly growing integration of AI in various sectors, including education, a systematic and robust tool for evaluating AI chatbot performance is essential.…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Test Construction
Kate E. Walton; Cristina Anguiano-Carrasco – ACT, Inc., 2024
Large language models (LLMs), such as ChatGPT, are becoming increasingly prominent. Their use is becoming more and more popular to assist with simple tasks, such as summarizing documents, translating languages, rephrasing sentences, or answering questions. Reports like McKinsey's (Chui, & Yee, 2023) estimate that by implementing LLMs,…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Test Construction
Edmund De Leon Evangelista – Contemporary Educational Technology, 2025
The rapid advancement of artificial intelligence (AI) technologies, particularly OpenAI's ChatGPT, has significantly impacted higher education institutions (HEIs), offering opportunities and challenges. While these tools enhance personalized learning and content generation, they threaten academic integrity, especially in assessment environments.…
Descriptors: Artificial Intelligence, Integrity, Educational Strategies, Natural Language Processing
Shuqiong Luo; Di Zou – European Journal of Education, 2025
Recent AI-based language learning research highlights learners' crucial role, yet university learner readiness in ChatGPT-based English learning remains unexplored. Accordingly, this current research attempted to develop and validate a tool to evaluate university learner readiness for ChatGPT-assisted English learning (LRCEL) to address the…
Descriptors: College Students, Readiness, Artificial Intelligence, Natural Language Processing
Naveed Saif; Sadaqat Ali; Abner Rubin; Soliman Aljarboa; Nabil Sharaf Almalki; Mrim M. Alnfiai; Faheem Khan; Sajid Ullah Khan – Educational Technology & Society, 2025
In the swiftly evolving landscape of education, the fusion of Artificial Intelligence's ingenuity with the dynamic capabilities of chat-bot technology has ignited a transformative paradigm shift. This convergence is not merely a technological integration but a profound reshaping of the fundamental principles of pedagogy, fundamentally redefining…
Descriptors: Artificial Intelligence, Technology Uses in Education, Readiness, Technological Literacy
MacKenzie D. Sidwell; Landon W. Bonner; Kayla Bates-Brantley; Shengtian Wu – Intervention in School and Clinic, 2024
Oral reading fluency probes are essential for reading assessment, intervention, and progress monitoring. Due to the limited options for choosing oral reading fluency probes, it is important to utilize all available resources such as generative artificial intelligence (AI) like ChatGPT to create oral reading fluency probes. The purpose of this…
Descriptors: Artificial Intelligence, Natural Language Processing, Technology Uses in Education, Oral Reading
Carmen Köhler; Johannes Hartig – Contemporary Educational Technology, 2024
Since ChatGPT-3.5 has been available to the public, the potentials and challenges regarding chatbot usage in education have been widely discussed. However, little evidence exists whether and for which purposes students even apply generative AI tools. The first main purpose of the present study was to develop and test scales that assess students'…
Descriptors: Artificial Intelligence, College Students, Natural Language Processing, Technology Uses in Education