AI Across Borders: Exploring Perceptions and Interactions in Higher Education
- URL: http://arxiv.org/abs/2501.00017v2
- Date: Thu, 14 Aug 2025 12:25:48 GMT
- Title: AI Across Borders: Exploring Perceptions and Interactions in Higher Education
- Authors: Juliana Gerard, Sahajpreet Singh, Morgan Macleod, Michael McKay, Antoine Rivoire, Tanmoy Chakraborty, Muskaan Singh,
- Abstract summary: This study investigates students' perceptions of Generative Artificial Intelligence (GenAI)<n>We collect quantitative Likert ratings and qualitative comments from 1211 students on their awareness and perceptions of AI.
- Score: 14.650938059200287
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This study investigates students' perceptions of Generative Artificial Intelligence (GenAI), with a focus on Higher Education institutions in Northern Ireland and India. We collect quantitative Likert ratings and qualitative comments from 1211 students on their awareness and perceptions of AI and investigate variations in attitudes toward AI across institutions and subject areas, as well as interactions between these variables with demographic variables (focusing on gender). We found the following: (a) while perceptions varied across institutions, responses for Computer Sciences students were similar, both in terms of topics and degree of positivity; and (b) after controlling for institution and subject area, we observed no effect of gender. These results are consistent with previous studies, which find that students' perceptions are predicted by prior experience; crucially, however, the results of this study contribute to the literature by identifying important interactions between key factors that can influence experience, revealing a more nuanced picture of students' perceptions and the role of experience. We consider the implications of these relations, and further considerations for the role of experience.
Related papers
- Learning Factors in AI-Augmented Education: A Comparative Study of Middle and High School Students [0.7710436567988378]
This study investigates whether four critical learning factors, experience, clarity, comfort and motivation, maintain coherent in AI-augmented educational settings.<n>The study was conducted in authentic classroom contexts where students interacted with AI tools as part of learning activities.<n>Using a multimethod quantitative analysis, which combined correlation analysis and text mining, we revealed markedly different dimensional structures between the two age groups.
arXiv Detail & Related papers (2025-12-24T15:43:58Z) - Measuring Computer Science Enthusiasm: A Questionnaire-Based Analysis of Age and Gender Effects on Students' Interest [4.580941470529078]
This study offers new insights into students' interest in computer science (CS) education by disentangling the effects of age and gender.<n>We conceptualize enthusiasm as a short-term, activating expression of interest that combines positive affect, perceived relevance, and intention to re-engage.<n>Using data from more than 400 students participating in online CS courses, we examined age- and gender-related patterns in enthusiasm.
arXiv Detail & Related papers (2025-12-09T10:43:46Z) - Socratic Mind: Impact of a Novel GenAI-Powered Assessment Tool on Student Learning and Higher-Order Thinking [2.192176712066146]
This study examines the impact of Socratic Mind, a Generative Artificial Intelligence (GenAI) powered formative assessment tool on learning outcomes.<n>Students who engaged with the GenAI tool experienced significant gains in their quiz scores compared to those who did not.<n>Our findings highlight the promise of AI-mediated dialogue in fostering deeper engagement and higher-order cognitive skills.
arXiv Detail & Related papers (2025-09-18T03:08:24Z) - Understanding Student Attitudes and Acceptability of GenAI Tools in Higher Ed: Scale Development and Evaluation [22.35484281040714]
This study introduces a validated survey instrument designed to assess students' perceptions of generative AI (GenAI)<n>The instrument includes six thematic domains: institutional understanding, fairness and trust, academic and career influence, societal concerns, and GenAI use in writing and coursework.<n>Students from multilingual households perceived greater clarity in institutional policy, while first-generation students reported a stronger belief in GenAI's impact on future careers.
arXiv Detail & Related papers (2025-08-03T21:22:34Z) - What Makes a Good Natural Language Prompt? [72.3282960118995]
We conduct a meta-analysis surveying more than 150 prompting-related papers from leading NLP and AI conferences from 2022 to 2025.<n>We propose a property- and human-centric framework for evaluating prompt quality, encompassing 21 properties categorized into six dimensions.<n>We then empirically explore multi-property prompt enhancements in reasoning tasks, observing that single-property enhancements often have the greatest impact.
arXiv Detail & Related papers (2025-06-07T23:19:27Z) - Overcoming Obstacles: Challenges of Gender Inequality in Undergraduate ICT Programs [0.34865180646161636]
In Brazil, women represent less than 18% of ICT students in higher education.<n>This study explores the perceptions of women undergraduate students in ICT regarding gender inequality.
arXiv Detail & Related papers (2025-05-02T18:28:48Z) - "All Roads Lead to ChatGPT": How Generative AI is Eroding Social Interactions and Student Learning Communities [0.4188114563181615]
We investigate the potential impacts of generative AI on social interactions, peer learning, and classroom dynamics.
Our findings suggest that help-seeking requests are now often mediated by generative AI.
Students reported feeling increasingly isolated and demotivated as the social support systems they rely on begin to break.
arXiv Detail & Related papers (2025-04-14T00:40:58Z) - Assessing Computer Science Student Attitudes Towards AI Ethics and Policy [8.927858368749204]
The attitudes and competencies with respect to AI ethics and policy among post-secondary students studying computer science (CS) are of particular interest.
Despite computer scientists being at the forefront of learning about and using AI tools, their attitudes towards AI remain understudied.
arXiv Detail & Related papers (2025-04-06T23:03:47Z) - The Role of AI, Blockchain, Cloud, and Data (ABCD) in Enhancing Learning Assessments of College Students [0.0]
This study investigates how ABCD technologies can improve learning assessments in higher education.
The objective is to research how students perceive things, plan their behavior, and how ABCD technologies affect individual learning, academic integrity, co-learning, and trust in the assessment.
arXiv Detail & Related papers (2025-02-17T15:11:44Z) - Perceptions of Discriminatory Decisions of Artificial Intelligence: Unpacking the Role of Individual Characteristics [0.0]
Personal differences (digital self-efficacy, technical knowledge, belief in equality, political ideology) are associated with perceptions of AI outcomes.
Digital self-efficacy and technical knowledge are positively associated with attitudes toward AI.
Liberal ideologies are negatively associated with outcome trust, higher negative emotion, and greater skepticism.
arXiv Detail & Related papers (2024-10-17T06:18:26Z) - Understanding Student and Academic Staff Perceptions of AI Use in Assessment and Feedback [0.0]
The rise of Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) in higher education necessitates assessment reform.
This study addresses a critical gap by exploring student and academic staff experiences with AI and GenAI tools.
An online survey collected data from 35 academic staff and 282 students across two universities in Vietnam and one in Singapore.
arXiv Detail & Related papers (2024-06-22T10:25:01Z) - The Impact of Human Aspects on the Interactions Between Software Developers and End-Users in Software Engineering: A Systematic Literature Review [10.307654003138401]
We present a systematic review of studies on human aspects affecting developer-user interactions.
We identified various human aspects affecting developer-user interactions in 46 studies.
Our findings suggest the importance of leveraging positive effects and addressing negative effects in developer-user interactions.
arXiv Detail & Related papers (2024-05-08T03:38:36Z) - Generative AI in Education: A Study of Educators' Awareness, Sentiments, and Influencing Factors [2.217351976766501]
This study delves into university instructors' experiences and attitudes toward AI language models.
We find no correlation between teaching style and attitude toward generative AI.
While CS educators show far more confidence in their technical understanding of generative AI tools, they show no more confidence in their ability to detect AI-generated work.
arXiv Detail & Related papers (2024-03-22T19:21:29Z) - A machine learning approach to predict university enrolment choices through students' high school background in Italy [42.57210316104905]
This paper explores the influence of Italian high school students' proficiency in mathematics and the Italian language on their university enrolment choices.
We investigate potential gender differences in response to similar previous educational choices and achievements.
The findings shed light on the complex interplay of academic proficiency, gender, and high school background in shaping students' choices regarding university education.
arXiv Detail & Related papers (2024-02-29T10:05:37Z) - "Just a little bit on the outside for the whole time": Social belonging
confidence and the persistence of Machine Learning and Artificial
Intelligence students [0.9217021281095907]
The growing field of machine learning (ML) and artificial intelligence (AI) presents a unique and unexplored case within persistence research.
We conduct an exploratory study to gain an initial understanding of persistence in this field.
We discuss differences in how students describe being motivated by social belonging and the importance of close mentorship.
arXiv Detail & Related papers (2023-10-30T19:59:38Z) - Expanding the Role of Affective Phenomena in Multimodal Interaction
Research [57.069159905961214]
We examined over 16,000 papers from selected conferences in multimodal interaction, affective computing, and natural language processing.
We identify 910 affect-related papers and present our analysis of the role of affective phenomena in these papers.
We find limited research on how affect and emotion predictions might be used by AI systems to enhance machine understanding of human social behaviors and cognitive states.
arXiv Detail & Related papers (2023-05-18T09:08:39Z) - How Different Groups Prioritize Ethical Values for Responsible AI [75.40051547428592]
Private companies, public sector organizations, and academic groups have outlined ethical values they consider important for responsible AI technologies.
While their recommendations converge on a set of central values, little is known about the values a more representative public would find important for the AI technologies they interact with and might be affected by.
We conducted a survey examining how individuals perceive and prioritize responsible AI values across three groups.
arXiv Detail & Related papers (2022-05-16T14:39:37Z) - Towards Unbiased Visual Emotion Recognition via Causal Intervention [63.74095927462]
We propose a novel Emotion Recognition Network (IERN) to alleviate the negative effects brought by the dataset bias.
A series of designed tests validate the effectiveness of IERN, and experiments on three emotion benchmarks demonstrate that IERN outperforms other state-of-the-art approaches.
arXiv Detail & Related papers (2021-07-26T10:40:59Z) - Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units
and a Unified Framework [83.21732533130846]
The paper focuses on large in-the-wild databases, i.e., Aff-Wild and Aff-Wild2.
It presents the design of two classes of deep neural networks trained with these databases.
A novel multi-task and holistic framework is presented which is able to jointly learn and effectively generalize and perform affect recognition.
arXiv Detail & Related papers (2021-03-29T17:36:20Z) - Towards Causal Representation Learning [96.110881654479]
The two fields of machine learning and graphical causality arose and developed separately.
There is now cross-pollination and increasing interest in both fields to benefit from the advances of the other.
arXiv Detail & Related papers (2021-02-22T15:26:57Z) - The Challenges of Assessing and Evaluating the Students at Distance [77.34726150561087]
The COVID-19 pandemic has caused a strong effect on higher education institutions with the closure of classroom teaching activities.
This short essay aims to explore the challenges posed to Portuguese higher education institutions and to analyze the challenges posed to evaluation models.
arXiv Detail & Related papers (2021-01-30T13:13:45Z) - Social Engagement versus Learning Engagement -- An Exploratory Study of
FutureLearn Learners [61.58283466715385]
Massive Open Online Courses (MOOCs) continue to see increasing enrolment, but only a small percent of enrolees completes the MOOCs.
This study is particularly concerned with how learners interact with peers, along with their study progression in MOOCs.
The study was conducted on the less explored FutureLearn platform, which employs a social constructivist approach and promotes collaborative learning.
arXiv Detail & Related papers (2020-08-11T16:09:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.