Publication Date
| In 2026 | 0 |
| Since 2025 | 12 |
| Since 2022 (last 5 years) | 16 |
| Since 2017 (last 10 years) | 16 |
| Since 2007 (last 20 years) | 19 |
Descriptor
Source
Author
| Ahn, June | 1 |
| Albers, Starleen | 1 |
| Ali, Alisha | 1 |
| Amanda Barany | 1 |
| Andres Felipe Zambrano | 1 |
| Belle Li | 1 |
| Benjamin Ultan Cowley | 1 |
| Biling Hu | 1 |
| C. Edward Watson | 1 |
| Cohen, William W. | 1 |
| Daniel J. Anderson | 1 |
| More ▼ | |
Publication Type
| Journal Articles | 18 |
| Reports - Research | 16 |
| Guides - Classroom - Learner | 2 |
| Reports - Descriptive | 2 |
| Information Analyses | 1 |
| Reports - Evaluative | 1 |
| Speeches/Meeting Papers | 1 |
Education Level
| Higher Education | 8 |
| Postsecondary Education | 8 |
| High Schools | 2 |
| Secondary Education | 2 |
| Grade 10 | 1 |
Audience
| Students | 2 |
Location
| China | 1 |
| Pennsylvania | 1 |
Laws, Policies, & Programs
Assessments and Surveys
What Works Clearinghouse Rating
Dominic Lohr; Hieke Keuning; Natalie Kiesler – Journal of Computer Assisted Learning, 2025
Background: Feedback as one of the most influential factors for learning has been subject to a great body of research. It plays a key role in the development of educational technology systems and is traditionally rooted in deterministic feedback defined by experts and their experience. However, with the rise of generative AI and especially large…
Descriptors: College Students, Programming, Artificial Intelligence, Feedback (Response)
Sonsoles Lopez-Pernas; Kamila Misiejuk; Rogers Kaliisa; Mohammed Saqr – IEEE Transactions on Learning Technologies, 2025
Despite the growing use of large language models (LLMs) in educational contexts, there is no evidence on how these can be operationalized by students to generate custom datasets suitable for teaching and learning. Moreover, in the context of network science, little is known about whether LLMs can replicate real-life network properties. This study…
Descriptors: Students, Artificial Intelligence, Man Machine Systems, Interaction
Gal Sasson Lazovsky; Tuval Raz; Yoed N. Kenett – Journal of Creative Behavior, 2025
As artificial intelligence and natural language processing methods rapidly develop, communication plays a pivotal role in every-day interactions. In this theoretical paper, we explore the overlap and commonalities between question-asking and prompt engineering. While seemingly distinct, these processes share a common foundation in essential skills…
Descriptors: Creativity, Questioning Techniques, Inquiry, Artificial Intelligence
Kasra Lekan; Zachary A. Pardos – Journal of Learning Analytics, 2025
Choosing an undergraduate major is an important decision that impacts academic and career outcomes. In this work, we investigate augmenting personalized human advising for major selection using a large language model (LLM), GPT-4. Through a three-phase survey, we compare GPT suggestions and responses for undeclared first- and second-year students…
Descriptors: Technology Uses in Education, Artificial Intelligence, Academic Advising, Majors (Students)
Xiner Liu; Andres Felipe Zambrano; Ryan S. Baker; Amanda Barany; Jaclyn Ocumpaugh; Jiayi Zhang; Maciej Pankiewicz; Nidhi Nasiar; Zhanlan Wei – Journal of Learning Analytics, 2025
This study explores the potential of the large language model GPT-4 as an automated tool for qualitative data analysis by educational researchers, exploring which techniques are most successful for different types of constructs. Specifically, we assess three different prompt engineering strategies -- Zero-shot, Few-shot, and Fewshot with…
Descriptors: Coding, Artificial Intelligence, Automation, Data Analysis
Kadir Karakaya – Asian Journal of Distance Education, 2025
This article explores human-AI interaction with large language models or conversational agents in complex information tasks with a focus on prompt engineering strategies. The paper reviews the current literature on the use of artificial intelligence (AI) for complex information tasks that are often nonlinear and entail interpretation,…
Descriptors: Artificial Intelligence, Technology Uses in Education, Man Machine Systems, Interaction
Jacob Holster – Music Educators Journal, 2024
ChatGPT is emerging as a formidable asset for music educators, poised to enhance student engagement, refine assessment methods, and automate repetitive tasks related to music teaching. The core of this perspective revolves around the use of custom prompt templates that support individualized and reflexive teaching practices. The broader…
Descriptors: Music Education, Artificial Intelligence, Natural Language Processing, Man Machine Systems
Pablo Flores Romero; Kin Nok Nicholas Fung; Guang Rong; Benjamin Ultan Cowley – npj Science of Learning, 2025
Large Language Models (LLMs) present a radically new paradigm for the study of "information foraging behavior." We study how LLM technology is used for pedagogical content creation by a sample of 25 participants in a doctoral-level Artificial Intelligence (AI) in Education course, and the role of computational-thinking skills in shaping…
Descriptors: Man Machine Systems, Artificial Intelligence, Natural Language Processing, Interaction
Dinuka B. Herath; Egena Ode; Gayanga B. Herath – British Educational Research Journal, 2025
This study provides a comparative assessment of the capabilities of leading artificial intelligence (AI) tools and human participants in a business management education context. Specifically, we (a) assess how well current language models perform in providing answers to standardised essay-type assessments in a business and management education…
Descriptors: Artificial Intelligence, Technology Uses in Education, Man Machine Systems, Educational Benefits
Eman Abdullah AlOmar – ACM Transactions on Computing Education, 2025
Large Language Models (LLMs), such as ChatGPT, have become widely popular for various software engineering tasks, including programming, testing, code review, and program comprehension. However, their impact on improving software quality in educational settings remains uncertain. This article explores our experience teaching the use of Programming…
Descriptors: Coding, Natural Language Processing, Artificial Intelligence, Computer Software
Gang Zhao; Lijun Yang; Biling Hu; Jing Wang – Journal of Educational Computing Research, 2025
Human-computer collaboration is an effective way to learn programming courses. However, most existing human-computer collaborative programming learning is supported by traditional computers with a relatively low level of personalized interaction, which greatly limits the efficiency of students' efficiency of programming learning and development of…
Descriptors: Artificial Intelligence, Man Machine Systems, Programming, Learning Strategies
Nguyen, Ha; Lopez, John; Homer, Bruce; Ali, Alisha; Ahn, June – Information and Learning Sciences, 2023
Purpose: In the USA, 22-40% of youth who have been accepted to college do not enroll. Researchers call this phenomenon summer melt, which disproportionately affects students from disadvantaged backgrounds. A major challenge is providing enough mentorship with the limited number of available college counselors. The purpose of this study is to…
Descriptors: Design, Artificial Intelligence, Man Machine Systems, Interaction
Jionghao Lin; Shaveen Singh; Lela Sha; Wei Tan; David Lang; Dragan Gasevic; Guanliang Chen – Grantee Submission, 2022
To construct dialogue-based Intelligent Tutoring Systems (ITS) with sufficient pedagogical expertise, a trendy research method is to mine large-scale data collected by existing dialogue-based ITS or generated between human tutors and students to discover effective tutoring strategies. However, most of the existing research has mainly focused on…
Descriptors: Intelligent Tutoring Systems, Teaching Methods, Dialogs (Language), Man Machine Systems
Mohan Yang; Shiyan Jiang; Belle Li; Kristin Herman; Tian Luo; Shanan Chappell Moots; Nolan Lovett – British Journal of Educational Technology, 2025
Generative artificial intelligence brings opportunities and unique challenges to nontraditional higher education students, stemming, in part, from the experience of the digital divide. Providing access and practice is critical to bridge this divide and equip students with needed digital competencies. This mixed-methods study investigated how…
Descriptors: Nontraditional Students, Artificial Intelligence, Technology Uses in Education, Man Machine Systems
Daniel J. Anderson; C. Edward Watson; Lee Rainie; Janna Anderson – American Association of Colleges and Universities, 2025
This free guide empowers students with the insights they need to navigate the age of AI academically, professionally, and ethically. It includes guidance for responsible academic AI use, a checklist for ethical and effective engagement with AI, and tips on incorporating AI to enhance student capabilities. It also outlines how to build an effective…
Descriptors: College Students, Student Experience, Technology Uses in Education, Artificial Intelligence
Previous Page | Next Page »
Pages: 1 | 2
Peer reviewed
Direct link
