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Selcuk Acar; Peter Organisciak; Denis Dumas – Journal of Creative Behavior, 2025
In this three-study investigation, we applied various approaches to score drawings created in response to both Form A and Form B of the Torrance Tests of Creative Thinking-Figural (broadly TTCT-F) as well as the Multi-Trial Creative Ideation task (MTCI). We focused on TTCT-F in Study 1, and utilizing a random forest classifier, we achieved 79% and…
Descriptors: Scoring, Computer Assisted Testing, Models, Correlation
Peter Baldwin; Victoria Yaneva; Kai North; Le An Ha; Yiyun Zhou; Alex J. Mechaber; Brian E. Clauser – Journal of Educational Measurement, 2025
Recent developments in the use of large-language models have led to substantial improvements in the accuracy of content-based automated scoring of free-text responses. The reported accuracy levels suggest that automated systems could have widespread applicability in assessment. However, before they are used in operational testing, other aspects of…
Descriptors: Artificial Intelligence, Scoring, Computational Linguistics, Accuracy
Swapna Haresh Teckwani; Amanda Huee-Ping Wong; Nathasha Vihangi Luke; Ivan Cherh Chiet Low – Advances in Physiology Education, 2024
The advent of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT and Gemini, has significantly impacted the educational landscape, offering unique opportunities for learning and assessment. In the realm of written assessment grading, traditionally viewed as a laborious and subjective process, this study sought to…
Descriptors: Accuracy, Reliability, Computational Linguistics, Standards
Ernesto Panadero; Alazne Fernández Ortube; Rebecca Krebs; Julian Roelle – Assessment & Evaluation in Higher Education, 2025
Rubrics play a crucial role in shaping educational assessment, providing clear criteria for both teaching and learning. The advent of online rubric platforms has the potential to significantly enhance the effectiveness of rubrics in educational contexts, offering innovative features for assessment and feedback through the creation of erubrics.…
Descriptors: Scoring Rubrics, Teaching Methods, Learning Processes, Feedback (Response)
Rebecka Weegar; Peter Idestam-Almquist – International Journal of Artificial Intelligence in Education, 2024
Machine learning methods can be used to reduce the manual workload in exam grading, making it possible for teachers to spend more time on other tasks. However, when it comes to grading exams, fully eliminating manual work is not yet possible even with very accurate automated grading, as any grading mistakes could have significant consequences for…
Descriptors: Grading, Computer Assisted Testing, Introductory Courses, Computer Science Education
Dadi Ramesh; Suresh Kumar Sanampudi – European Journal of Education, 2024
Automatic essay scoring (AES) is an essential educational application in natural language processing. This automated process will alleviate the burden by increasing the reliability and consistency of the assessment. With the advances in text embedding libraries and neural network models, AES systems achieved good results in terms of accuracy.…
Descriptors: Scoring, Essays, Writing Evaluation, Memory
Elizabeth L. Wetzler; Kenneth S. Cassidy; Margaret J. Jones; Chelsea R. Frazier; Nickalous A. Korbut; Chelsea M. Sims; Shari S. Bowen; Michael Wood – Teaching of Psychology, 2025
Background: Generative artificial intelligence (AI) represents a potentially powerful, time-saving tool for grading student essays. However, little is known about how AI-generated essay scores compare to human instructor scores. Objective: The purpose of this study was to compare the essay grading scores produced by AI with those of human…
Descriptors: Essays, Writing Evaluation, Scores, Evaluators
Andrea Horbach; Joey Pehlke; Ronja Laarmann-Quante; Yuning Ding – International Journal of Artificial Intelligence in Education, 2024
This paper investigates crosslingual content scoring, a scenario where scoring models trained on learner data in one language are applied to data in a different language. We analyze data in five different languages (Chinese, English, French, German and Spanish) collected for three prompts of the established English ASAP content scoring dataset. We…
Descriptors: Contrastive Linguistics, Scoring, Learning Analytics, Chinese
Zehavit Kohen; Liron Schwartz-Aviad; Tomer Peleg – International Journal of Mathematical Education in Science and Technology, 2025
This study examines a Technology, Pedagogy, Content Knowledge (TPACK) training framework for pre- and in-service mathematics teachers that enables them to experience inquiry-based learning as learners in a dynamic geometry environment that serves as a mathematics laboratory. We investigate the effect of this experience on the teachers' TPACK,…
Descriptors: Pedagogical Content Knowledge, Technological Literacy, Mathematics Teachers, Preservice Teachers
Peter Daly; Emmanuelle Deglaire – Innovations in Education and Teaching International, 2025
AI-enabled assessment of student papers has the potential to provide both summative and formative feedback and reduce the time spent on grading. Using auto-ethnography, this study compares AI-enabled and human assessment of business student examination papers in a law module based on previously established rubrics. Examination papers were…
Descriptors: Artificial Intelligence, Computer Software, Technology Integration, College Faculty
Eran Hadas; Arnon Hershkovitz – Journal of Learning Analytics, 2025
Creativity is an imperative skill for today's learners, one that has important contributions to issues of inclusion and equity in education. Therefore, assessing creativity is of major importance in educational contexts. However, scoring creativity based on traditional tools suffers from subjectivity and is heavily time- and labour-consuming. This…
Descriptors: Creativity, Evaluation Methods, Computer Assisted Testing, Artificial Intelligence
Zewei Tian; Lief Esbenshade; Alex Liu; Shawon Sarkar; Zachary Zhang; Kevin He; Min Sun – Grantee Submission, 2025
The Colleague AI platform introduces a groundbreaking Rubric Generation function designed to streamline how educators create and use rubrics for instructional and assessment purposes. This feature uses artificial intelligence (AI) to produce standards-based rubrics tailored to course content for formative and summative evaluations. By automating…
Descriptors: Scoring Rubrics, Artificial Intelligence, Futures (of Society), Teaching Methods
Jenny L. Richmond; Kate Nicholls – Teaching of Psychology, 2025
Background: With the arrival of generative artificial intelligence (genAI) tools, psychology educators are rethinking their assessment practices. Objective: This paper describes one approach to integrating genAI into an assessment designed to promote psychological literacy. Method: Students used ChatGPT to generate a media release about a…
Descriptors: Artificial Intelligence, Scoring Rubrics, Computer Software, Learning Processes
Thomas Trendowski – Strategies: A Journal for Physical and Sport Educators, 2025
Generative artificial intelligence (GAI) is becoming more popular among educators with the recent advancements in tools such as ChatGPT and Dall-E. Although there are still questions around its efficacy, this type of technology can produce data or information based on prompts, which can be adapted for teachers' use. This article explores how this…
Descriptors: Physical Education, Team Sports, Curriculum Design, Physical Education Teachers
Daniel Holcombe – Hispania, 2025
Accompanying the recent rise in cautious popularity surrounding Generative Artificial Intelligence (Gen-AI), some language educators are exploring innovative linguistic interactions with Gen-AI. Seeking to add a literature approximation to such criticism, this article explores two activities that feature Gen-AI in undergraduate literature courses.…
Descriptors: Undergraduate Students, Artificial Intelligence, Computer Software, Cheating