Understanding Student Acceptance, Trust, and Attitudes Toward AI-Generated Images for Educational Purposes
- URL: http://arxiv.org/abs/2411.15710v1
- Date: Sun, 24 Nov 2024 04:39:48 GMT
- Title: Understanding Student Acceptance, Trust, and Attitudes Toward AI-Generated Images for Educational Purposes
- Authors: Aung Pyae,
- Abstract summary: This study assesses students' acceptance, trust, and positive attitudes towards AI-generated images for educational tasks.
The results reveal high acceptance, trust, and positive attitudes among students who value the ease of use and potential academic benefits.
These findings suggest a need for developing comprehensive guidelines that address ethical considerations and intellectual property issues.
- Score: 1.0878040851637998
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advancements in artificial intelligence (AI) have broadened the applicability of AI-generated images across various sectors, including the creative industry and design. However, their utilization in educational contexts, particularly among undergraduate students in computer science and software engineering, remains underexplored. This study adopts an exploratory approach, employing questionnaires and interviews, to assess students' acceptance, trust, and positive attitudes towards AI-generated images for educational tasks such as presentations, reports, and web design. The results reveal high acceptance, trust, and positive attitudes among students who value the ease of use and potential academic benefits. However, concerns regarding the lack of technical precision, where the AI fails to accurately produce images as specified by prompts, moderately impact their practical application in detail-oriented educational tasks. These findings suggest a need for developing comprehensive guidelines that address ethical considerations and intellectual property issues, while also setting quality standards for AI-generated images to enhance their educational use. Enhancing the capabilities of AI tools to meet precise user specifications could foster creativity and improve educational outcomes in technical disciplines.
Related papers
- Enhancing AI-Driven Education: Integrating Cognitive Frameworks, Linguistic Feedback Analysis, and Ethical Considerations for Improved Content Generation [0.0]
This paper synthesizes insights from four related studies to propose a comprehensive framework for enhancing AI-driven educational tools.
We integrate cognitive assessment frameworks, linguistic analysis of AI-generated feedback, and ethical design principles to guide the development of effective and responsible AI tools.
arXiv Detail & Related papers (2025-05-01T06:36:21Z) - "From Unseen Needs to Classroom Solutions": Exploring AI Literacy Challenges & Opportunities with Project-based Learning Toolkit in K-12 Education [0.3994567502796064]
There is a growing need to equip K-12 students with AI literacy skills that extend beyond computer science.
This paper explores the integration of a Project-Based Learning (PBL) AI toolkit into diverse subject areas, aimed at helping educators teach AI concepts more effectively.
arXiv Detail & Related papers (2024-12-23T03:31:02Z) - AI-generated Image Quality Assessment in Visual Communication [72.11144790293086]
AIGI-VC is a quality assessment database for AI-generated images in visual communication.
The dataset consists of 2,500 images spanning 14 advertisement topics and 8 emotion types.
It provides coarse-grained human preference annotations and fine-grained preference descriptions, benchmarking the abilities of IQA methods in preference prediction, interpretation, and reasoning.
arXiv Detail & Related papers (2024-12-20T08:47:07Z) - AI in Education: Rationale, Principles, and Instructional Implications [0.0]
Generative AI, like ChatGPT, can create human-like content, prompting questions about its educational role.
The study emphasizes deliberate strategies to ensure AI complements, not replaces, genuine cognitive effort.
arXiv Detail & Related papers (2024-12-02T14:08:07Z) - Human-Centric eXplainable AI in Education [0.0]
This paper explores Human-Centric eXplainable AI (HCXAI) in the educational landscape.
It emphasizes its role in enhancing learning outcomes, fostering trust among users, and ensuring transparency in AI-driven tools.
It outlines comprehensive frameworks for developing HCXAI systems that prioritize user understanding and engagement.
arXiv Detail & Related papers (2024-10-18T14:02:47Z) - Collaborative Design of AI-Enhanced Learning Activities [0.0]
We develop a formative intervention that enables preservice teachers, in-service teachers, and EdTech specialists to effectively incorporate AI into their teaching practices.
Participants reflect on AI's potential in teaching and learning by exploring different activities that can integrate AI literacy in education, including its ethical considerations and potential for innovative pedagogy.
arXiv Detail & Related papers (2024-07-09T08:34:08Z) - The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges [2.569083526579529]
AI in education raises ethical concerns regarding validity, reliability, transparency, fairness, and equity.
Various stakeholders, including educators, policymakers, and organizations, have developed guidelines to ensure ethical AI use in education.
In this paper, a diverse group of AIME members examines the ethical implications of AI-powered tools in educational measurement.
arXiv Detail & Related papers (2024-06-27T05:28:40Z) - Toward enriched Cognitive Learning with XAI [44.99833362998488]
We introduce an intelligent system (CL-XAI) for Cognitive Learning which is supported by artificial intelligence (AI) tools.
The use of CL-XAI is illustrated with a game-inspired virtual use case where learners tackle problems to enhance problem-solving skills.
arXiv Detail & Related papers (2023-12-19T16:13:47Z) - AI-Generated Images as Data Source: The Dawn of Synthetic Era [61.879821573066216]
generative AI has unlocked the potential to create synthetic images that closely resemble real-world photographs.
This paper explores the innovative concept of harnessing these AI-generated images as new data sources.
In contrast to real data, AI-generated data exhibit remarkable advantages, including unmatched abundance and scalability.
arXiv Detail & Related papers (2023-10-03T06:55:19Z) - Artificial Intelligence-Enabled Intelligent Assistant for Personalized
and Adaptive Learning in Higher Education [0.2812395851874055]
This paper presents a novel framework, Artificial Intelligence-Enabled Intelligent Assistant (AIIA) for personalized and adaptive learning in higher education.
The AIIA system leverages advanced AI and Natural Language Processing (NLP) techniques to create an interactive and engaging learning platform.
arXiv Detail & Related papers (2023-09-19T19:31:15Z) - Experts' View on Challenges and Needs for Fairness in Artificial
Intelligence for Education [11.374344511408443]
We conducted the first expert-driven systematic investigation on the challenges and needs for addressing fairness throughout the development of educational systems based on AI.
We identified common and diverging views about the challenges and the needs faced by educational technologies experts in practice.
arXiv Detail & Related papers (2022-06-23T13:29:39Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Personalized Education in the AI Era: What to Expect Next? [76.37000521334585]
The objective of personalized learning is to design an effective knowledge acquisition track that matches the learner's strengths and bypasses her weaknesses to meet her desired goal.
In recent years, the boost of artificial intelligence (AI) and machine learning (ML) has unfolded novel perspectives to enhance personalized education.
arXiv Detail & Related papers (2021-01-19T12:23:32Z) - Empowering Things with Intelligence: A Survey of the Progress,
Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
We show how AI can empower the IoT to make it faster, smarter, greener, and safer.
First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.
Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
arXiv Detail & Related papers (2020-11-17T13:14:28Z) - A Review on Intelligent Object Perception Methods Combining
Knowledge-based Reasoning and Machine Learning [60.335974351919816]
Object perception is a fundamental sub-field of Computer Vision.
Recent works seek ways to integrate knowledge engineering in order to expand the level of intelligence of the visual interpretation of objects.
arXiv Detail & Related papers (2019-12-26T13:26:49Z)
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.