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Owen Henkel; Hannah Horne-Robinson; Libby Hills; Bill Roberts; Josh McGrane – International Journal of Artificial Intelligence in Education, 2025
This paper reports on a set of three recent experiments utilizing large-scale speech models to assess the oral reading fluency (ORF) of students in Ghana. While ORF is a well-established measure of foundational literacy, assessing it typically requires one-on-one sessions between a student and a trained rater, a process that is time-consuming and…
Descriptors: Foreign Countries, Oral Reading, Reading Fluency, Literacy
Luiz Rodrigues; Guilherme Guerino; Thomaz E. V. Silva; Geiser C. Challco; Lívia Oliveira; Rodolfo S. da Penha; Rafael F. Melo; Thales Vieira; Marcelo Marinho; Valmir Macario; Ig I. Bittencourt; Diego Dermeval; Seiji Isotani – International Journal of Artificial Intelligence in Education, 2025
Intelligent Tutoring Systems (ITS) possess significant potential to enhance learning outcomes. However, deploying ITSs in global south countries presents challenges due to their frequent lack of essential technological resources, such as computers and internet access. The concept of AIED Unplugged has emerged to bridge this digital divide,…
Descriptors: Teacher Attitudes, Intelligent Tutoring Systems, Numeracy, Mathematics Education
Eglington, Luke G.; Pavlik, Philip I., Jr. – International Journal of Artificial Intelligence in Education, 2023
An important component of many Adaptive Instructional Systems (AIS) is a 'Learner Model' intended to track student learning and predict future performance. Predictions from learner models are frequently used in combination with mastery criterion decision rules to make pedagogical decisions. Important aspects of learner models, such as learning…
Descriptors: Computer Assisted Instruction, Intelligent Tutoring Systems, Learning Processes, Individual Differences
Griffith, Amanda E.; Katuka, Gloria Ashiya; Wiggins, Joseph B.; Boyer, Kristy Elizabeth; Freeman, Jason; Magerko, Brian; McKlin, Tom – International Journal of Artificial Intelligence in Education, 2023
Collaborative learning offers numerous benefits to learners, largely due to the dialogue that is unfolding between them. However, there is still much to learn about the structure of collaborative dialogue, and especially little is known about co-creative dialogues during learning. This paper reports on a study with learners engaged in co-creative…
Descriptors: Cooperative Learning, Dialogs (Language), Coding, Student Satisfaction
Schneider, Johannes; Richner, Robin; Riser, Micha – International Journal of Artificial Intelligence in Education, 2023
Autograding short textual answers has become much more feasible due to the rise of NLP and the increased availability of question-answer pairs brought about by a shift to online education. Autograding performance is still inferior to human grading. The statistical and black-box nature of state-of-the-art machine learning models makes them…
Descriptors: Grading, Natural Language Processing, Computer Assisted Testing, Ethics
Tacoma, Sietske; Heeren, Bastiaan; Jeuring, Johan; Drijvers, Paul – International Journal of Artificial Intelligence in Education, 2020
Hypothesis testing involves a complex stepwise procedure that is challenging for many students in introductory university statistics courses. In this paper we assess how feedback from an Intelligent Tutoring System can address the logic of hypothesis testing and whether such feedback contributes to first-year social sciences students' proficiency…
Descriptors: Hypothesis Testing, Feedback (Response), Intelligent Tutoring Systems, Introductory Courses
Vajjala, Sowmya – International Journal of Artificial Intelligence in Education, 2018
Automatic essay scoring (AES) refers to the process of scoring free text responses to given prompts, considering human grader scores as the gold standard. Writing such essays is an essential component of many language and aptitude exams. Hence, AES became an active and established area of research, and there are many proprietary systems used in…
Descriptors: Computer Software, Essays, Writing Evaluation, Scoring
Ohlsson, Stellan – International Journal of Artificial Intelligence in Education, 2016
The ideas behind the constraint-based modeling (CBM) approach to the design of intelligent tutoring systems (ITSs) grew out of attempts in the 1980's to clarify how declarative and procedural knowledge interact during skill acquisition. The learning theory that underpins CBM was based on two conceptual innovations. The first innovation was to…
Descriptors: Intelligent Tutoring Systems, Artificial Intelligence, Models, Learning Theories