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Tal Waltzer; Celeste Pilegard; Gail D. Heyman – International Journal for Educational Integrity, 2024
The release of ChatGPT in 2022 has generated extensive speculation about how Artificial Intelligence (AI) will impact the capacity of institutions for higher learning to achieve their central missions of promoting learning and certifying knowledge. Our main questions were whether people could identify AI-generated text and whether factors such as…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, College Students
Matthew Landers – Higher Education for the Future, 2025
This article presents a brief overview of the state-of-the-art in large language models (LLMs) like ChatGPT and discusses the difficulties that these technologies create for educators with regard to assessment. Making use of the 'arms race' metaphor, this article argues that there are no simple solutions to the 'AI problem'. Rather, this author…
Descriptors: Ethics, Cheating, Plagiarism, Artificial Intelligence
Abdulla-All Mijan; Md Rabiul Hasan; Mehedi Hasan – International Journal of Technology in Education and Science, 2025
This study investigated the utilization of artificial intelligence (AI) platforms in Bangladeshi higher education institutions, with an emphasis on determining which AI platforms are most widely used and assessing the variables that affect AI adoption. The particular criteria influencing platform preferences and the comparative analysis of various…
Descriptors: Artificial Intelligence, Technology Integration, Technology Uses in Education, Foreign Countries
Kim, Min Kyu; Gaul, Cassandra J.; Kim, So Mi; Madathany, Reeny J. – Technology, Knowledge and Learning, 2020
While key concepts embedded within an expert's textual explanation have been considered an aspect of expert model, the complexity of textual data makes determining key concepts demanding and time consuming. To address this issue, we developed Student Mental Model Analyzer for Teaching and Learning (SMART) technology that can analyze an experts'…
Descriptors: Natural Language Processing, Educational Technology, Concept Mapping, Accuracy
Kolb, John; Farrar, Scott; Pardos, Zachary A. – International Educational Data Mining Society, 2019
Misconceptions have been an important area of study in STEM education towards improving our understanding of learners' construction of knowledge. The advent of largescale tutoring systems has given rise to an abundance of data in the form of learner question-answer logs in which signatures of misconceptions can be mined. In this work, we explore…
Descriptors: Misconceptions, Expertise, Mathematics Teachers, Semantics
Nelson, Laura K. – Sociological Methods & Research, 2020
This article proposes a three-step methodological framework called computational grounded theory, which combines expert human knowledge and hermeneutic skills with the processing power and pattern recognition of computers, producing a more methodologically rigorous but interpretive approach to content analysis. The first, pattern detection step,…
Descriptors: Grounded Theory, Content Analysis, Expertise, Hermeneutics
Sheri Conklin; Tom Dorgan; Daisyane Barreto – Discover Education, 2024
We investigated the utility of ChatGPT 3.5 in the creation of a fully online asynchronous higher education course. Our collaborative effort with ChatGPT resulted in developing a Master's level course on Trends and Issues in Instructional Design using the Backward Design Model. Throughout this process, we recognized the critical role of precise…
Descriptors: Design, Technology Uses in Education, Artificial Intelligence, Instructional Design
Thomas K. F. Chiu; Benjamin Luke Moorhouse; Ching Sing Chai; Murod Ismailov – Interactive Learning Environments, 2024
As Artificial Intelligence (AI) advances technologically, it will inevitably bring many changes to classroom practices. However, research on AI in education reflects a weak connection to pedagogical perspectives or instructional approaches, particularly in K-12 education. AI technologies may benefit motivated and advanced students. Understanding…
Descriptors: Teacher Student Relationship, Student Motivation, Artificial Intelligence, Technology Uses in Education
Echedom, Anthonia U.; Okuonghae, Omorodion – New Review of Academic Librarianship, 2021
This paper focuses on the opportunities and challenges associated with the use of artificial intelligence (AI) in academic library operations. In the quest to render fast, effective and efficient services, academic libraries have adopted different technologies in the past. Artificial intelligence technologies is the latest among the technologies…
Descriptors: Academic Libraries, Library Services, Delivery Systems, Artificial Intelligence
Li, Haiying; Cai, Zhiqiang; Graesser, Arthur – Grantee Submission, 2018
In this study we developed and evaluated a crowdsourcing-based latent semantic analysis (LSA) approach to computerized summary scoring (CSS). LSA is a frequently used mathematical component in CSS, where LSA similarity represents the extent to which the to-be-graded target summary is similar to a model summary or a set of exemplar summaries.…
Descriptors: Computer Assisted Testing, Scoring, Semantics, Evaluation Methods
Crowe, Dale; LaPierre, Martin; Kebritchi, Mansureh – TechTrends: Linking Research and Practice to Improve Learning, 2017
With augmented intelligence/knowledge based system (KBS) it is now possible to develop distance learning applications to support both curriculum and administrative tasks. Instructional designers and information technology (IT) professionals are now moving from the programmable systems era that started in the 1950s to the cognitive computing era.…
Descriptors: Artificial Intelligence, Information Technology, Distance Education, Instructional Design
Katz, Marco; van Bruggen, Jan; Giesbers, Bas; Waterink, Wim; Eshuis, Jannes; Koper, Rob – Educational Technology & Society, 2014
This paper discusses Latent Semantic Analysis (LSA) as a method for the assessment of prior learning. The Accreditation of Prior Learning (APL) is a procedure to offer learners an individualized curriculum based on their prior experiences and knowledge. The placement decisions in this process are based on the analysis of student material by domain…
Descriptors: Semantics, Student Evaluation, Prior Learning, Individualized Instruction
Rus, Vasile; Moldovan, Cristian; Niraula, Nobal; Graesser, Arthur C. – International Educational Data Mining Society, 2012
In this paper we address the important task of automated discovery of speech act categories in dialogue-based, multi-party educational games. Speech acts are important in dialogue-based educational systems because they help infer the student speaker's intentions (the task of speech act classification) which in turn is crucial to providing adequate…
Descriptors: Educational Games, Feedback (Response), Classification, Expertise
Jarman, Jay – ProQuest LLC, 2011
This dissertation focuses on developing and evaluating hybrid approaches for analyzing free-form text in the medical domain. This research draws on natural language processing (NLP) techniques that are used to parse and extract concepts based on a controlled vocabulary. Once important concepts are extracted, additional machine learning algorithms,…
Descriptors: Natural Language Processing, Classification, Expertise, Information Technology
Pirnay-Dummer, Pablo; Ifenthaler, Dirk – Instructional Science: An International Journal of the Learning Sciences, 2011
Our study integrates automated natural language-oriented assessment and analysis methodologies into feasible reading comprehension tasks. With the newly developed T-MITOCAR toolset, prose text can be automatically converted into an association net which has similarities to a concept map. The "text to graph" feature of the software is based on…
Descriptors: Concept Mapping, Reading Comprehension, Graphs, Natural Language Processing
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