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Elisabeth Bauer; Michael Sailer; Frank Niklas; Samuel Greiff; Sven Sarbu-Rothsching; Jan M. Zottmann; Jan Kiesewetter; Matthias Stadler; Martin R. Fischer; Tina Seidel; Detlef Urhahne; Maximilian Sailer; Frank Fischer – Journal of Computer Assisted Learning, 2025
Background: Artificial intelligence, particularly natural language processing (NLP), enables automating the formative assessment of written task solutions to provide adaptive feedback automatically. A laboratory study found that, compared with static feedback (an expert solution), adaptive feedback automated through artificial neural networks…
Descriptors: Artificial Intelligence, Feedback (Response), Computer Simulation, Natural Language Processing
Carme Grimalt-Álvaro; Mireia Usart – Journal of Computing in Higher Education, 2024
Sentiment Analysis (SA), a technique based on applying artificial intelligence to analyze textual data in natural language, can help to characterize interactions between students and teachers and improve learning through timely, personalized feedback, but its use in education is still scarce. This systematic literature review explores how SA has…
Descriptors: Formative Evaluation, Higher Education, Artificial Intelligence, Natural Language Processing
Chahna Gonsalves – Journal of Learning Development in Higher Education, 2023
Multiple-choice quizzes (MCQs) are a popular form of assessment. A rapid shift to online assessment during the COVID-19 pandemic in 2020, drove the uptake of MCQs, yet limited invigilation and wide access to material on the internet allow students to solve the questions via internet search. ChatGPT, an artificial intelligence (AI) agent trained on…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Multiple Choice Tests
Araz Zirar – Review of Education, 2023
Recent developments in language models, such as ChatGPT, have sparked debate. These tools can help, for example, dyslexic people, to write formal emails from a prompt and can be used by students to generate assessed work. Proponents argue that language models enhance the student experience and academic achievement. Those concerned argue that…
Descriptors: Artificial Intelligence, Technology Uses in Education, Natural Language Processing, Models
Öncel, Püren; Flynn, Lauren E.; Sonia, Allison N.; Barker, Kennis E.; Lindsay, Grace C.; McClure, Caleb M.; McNamara, Danielle S.; Allen, Laura K. – Grantee Submission, 2021
Automated Writing Evaluation systems have been developed to help students improve their writing skills through the automated delivery of both summative and formative feedback. These systems have demonstrated strong potential in a variety of educational contexts; however, they remain limited in their personalization and scope. The purpose of the…
Descriptors: Computer Assisted Instruction, Writing Evaluation, Formative Evaluation, Summative Evaluation
Lynette Hazelton; Jessica Nastal; Norbert Elliot; Jill Burstein; Daniel F. McCaffrey – Journal of Response to Writing, 2021
In writing studies research, automated writing evaluation technology is typically examined for a specific, often narrow purpose: to evaluate a particular writing improvement measure, to mine data for changes in writing performance, or to demonstrate the effectiveness of a single technology and accompanying validity arguments. This article adopts a…
Descriptors: Formative Evaluation, Writing Evaluation, Automation, Natural Language Processing
Sinclair, Arabella J.; Schneider, Bertrand – International Educational Data Mining Society, 2021
Collaborative dialogue is rich in conscious and subconscious coordination behaviours between participants. This work explores collaborative learner dialogue through theories of alignment, analysing inter-partner movement and language use with respect to our hypotheses: that they interrelate, and that they form predictors of collaboration quality…
Descriptors: Dialogs (Language), Cooperative Learning, Correlation, Predictor Variables
Nguyen, Huy; Xiong, Wenting; Litman, Diane – International Journal of Artificial Intelligence in Education, 2017
A peer-review system that automatically evaluates and provides formative feedback on free-text feedback comments of students was iteratively designed and evaluated in college and high-school classrooms. Classroom assignments required students to write paper drafts and submit them to a peer-review system. When student peers later submitted feedback…
Descriptors: Computer Uses in Education, Computer Mediated Communication, Feedback (Response), Peer Evaluation
Jacovina, Matthew E.; McNamara, Danielle S. – Grantee Submission, 2017
In this chapter, we describe several intelligent tutoring systems (ITSs) designed to support student literacy through reading comprehension and writing instruction and practice. Although adaptive instruction can be a powerful tool in the literacy domain, developing these technologies poses significant challenges. For example, evaluating the…
Descriptors: Intelligent Tutoring Systems, Literacy Education, Educational Technology, Technology Uses in Education
International Association for Development of the Information Society, 2012
The IADIS CELDA 2012 Conference intention was to address the main issues concerned with evolving learning processes and supporting pedagogies and applications in the digital age. There had been advances in both cognitive psychology and computing that have affected the educational arena. The convergence of these two disciplines is increasing at a…
Descriptors: Academic Achievement, Academic Persistence, Academic Support Services, Access to Computers