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Atharva Naik; Jessica Ruhan Yin; Anusha Kamath; Qianou Ma; Sherry Tongshuang Wu; R. Charles Murray; Christopher Bogart; Majd Sakr; Carolyn P. Rose – British Journal of Educational Technology, 2025
The relative effectiveness of reflection either through student generation of contrasting cases or through provided contrasting cases is not well-established for adult learners. This paper presents a classroom study to investigate this comparison in a college level Computer Science (CS) course where groups of students worked collaboratively to…
Descriptors: Cooperative Learning, Reflection, College Students, Computer Science Education
Wali Khan Monib; Atika Qazi; Malissa Maria Mahmud – Education and Information Technologies, 2025
ChatGPT has emerged as a transformative technology with its remarkable ability to generate human-like responses, propelling its widespread adoption. While prior research has investigated the general landscape of AI-driven tools such as ChatGPT, the current study focuses specifically on exploring learners' experiences and perceptions regarding the…
Descriptors: Student Attitudes, Student Experience, Artificial Intelligence, Natural Language Processing

Yang Zhong; Mohamed Elaraby; Diane Litman; Ahmed Ashraf Butt; Muhsin Menekse – Grantee Submission, 2024
This paper introduces REFLECTSUMM, a novel summarization dataset specifically designed for summarizing students' reflective writing. The goal of REFLECTSUMM is to facilitate developing and evaluating novel summarization techniques tailored to real-world scenarios with little training data, with potential implications in the opinion summarization…
Descriptors: Documentation, Writing (Composition), Reflection, Metadata
Yuan Chih Fu; Jin Hua Chen; Kai Chieh Cheng; Xuan Fen Yuan – Higher Education: The International Journal of Higher Education Research, 2024
Using data from approximately 342,000 course-taking records collected from 4406 college students enrolled at Taipei Tech during the 2009-2012 academic years, we examine the impact of multidisciplinarity on students' academic performance. Our study contributes to the literature in three ways. First, by applying natural language processing (NLP), we…
Descriptors: College Students, Interdisciplinary Approach, Academic Achievement, Natural Language Processing
Muhammad Farrukh Shahzad; Shuo Xu; Hira Zahid – Education and Information Technologies, 2025
Artificial Intelligence (AI) technologies have rapidly transformed the education sector and affect student learning performance, particularly in China, a burgeoning educational landscape. The development of generative artificial intelligence (AI) based technologies, such as chatbots and large language models (LLMs) like ChatGPT, has completely…
Descriptors: Artificial Intelligence, Technology Uses in Education, Academic Achievement, Self Efficacy
Nisar Ahmed Dahri; Noraffandy Yahaya; Waleed Mugahed Al-Rahmi – Education and Information Technologies, 2025
Enhancing student academic success and career readiness is important in the rapidly evolving educational field. This study investigates the influence of ChatGPT, an AI tool, on these outcomes using the Stimulus-Organism-Response (SOR) theory and constructs from the Technology Acceptance Model (TAM). The aim is to explore how ChatGPT impacts…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Career Readiness
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
Jiaqi Yin; Tiong-Thye Goh; Yi Hu – International Journal of Educational Technology in Higher Education, 2024
Educational chatbots (EC) have shown their promise in providing instructional support. However, limited studies directly explored the impact of EC on learners' emotional responses. This study investigated the induced emotions from interacting with micro-learning EC and how they impact learning motivation. In this context, the EC interactions…
Descriptors: Artificial Intelligence, Man Machine Systems, Natural Language Processing, Psychological Patterns
Susan Shurden; Mike Shurden – Journal of Instructional Pedagogies, 2024
Artificial Intelligence (AI) is taking the world by storm. Higher education is not immune to this phenomenon and has many challenges in embracing AI. Much has been written lately concerning the typical application of AI in higher education, as well as in the classroom itself. The purpose of this paper is to gather information from students to…
Descriptors: Artificial Intelligence, Higher Education, College Students, Student Attitudes
Kevin Peyton; Saritha Unnikrishnan; Brian Mulligan – Discover Education, 2025
Within the university sector, student recruitment and enrolment are key strategies as institutions strive to attract, retain and engage students. This strategy is underpinned by the provision of services, applications and technologies that facilitate lecturing and support staff. Universities that offer online learning have a particular incentive…
Descriptors: Universities, Artificial Intelligence, Computer Mediated Communication, College Students
Ayse Merzifonluoglu; Habibe Gunes – European Journal of Education, 2025
Artificial intelligence (AI) is significantly shaping education and currently influencing pre-service teachers' academic and professional journeys. To explore this influence, the present study examines 389 Generation Z pre-service teachers' attitudes towards AI and its impact on educational decision-making at two state universities, using an…
Descriptors: Decision Making, Artificial Intelligence, Teacher Attitudes, Age Groups
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
Promethi Das Deep; Yixin Chen – Higher Education Studies, 2025
The COVID-19 pandemic significantly disrupted higher education. The sudden and profound transformations it necessitated had a direct and negative impact on higher education students, as evidenced by the widely reported instances of academic disengagement, decreased motivation, and lower performance. This was often due to student burnout caused by…
Descriptors: COVID-19, Pandemics, Electronic Learning, Fatigue (Biology)
Rashid, M. Parvez; Xiao, Yunkai; Gehringer, Edward F. – International Educational Data Mining Society, 2022
Peer assessment can be a more effective pedagogical method when reviewers provide quality feedback. But what makes feedback helpful to reviewees? Other studies have identified quality feedback as focusing on detecting problems, providing suggestions, or pointing out where changes need to be made. However, it is important to seek students'…
Descriptors: Peer Evaluation, Feedback (Response), Natural Language Processing, Artificial Intelligence
Allie Michael; Abdullah O. Akinde – Assessment Update, 2024
Open-ended responses to surveys can be highly beneficial to higher education institutions, providing clarity and context that quantitative data can sometimes lack. However, analyzing open-ended responses typically takes time and manpower most institutional assessment offices do not have to spare. This study focused on finding a potential solution…
Descriptors: Artificial Intelligence, Natural Language Processing, Student Surveys, Feedback (Response)