Mapping Students' AI Literacy Framing and Learning through Reflective Journals
- URL: http://arxiv.org/abs/2508.15112v1
- Date: Wed, 20 Aug 2025 23:01:51 GMT
- Title: Mapping Students' AI Literacy Framing and Learning through Reflective Journals
- Authors: Ashish Hingle, Aditya Johri,
- Abstract summary: This research paper presents a study of undergraduate technology students' self-reflective learning about artificial intelligence (AI)<n>It is important to understand what, how, and why students learn about AI so formal instruction can better support their learning.
- Score: 0.13154296174423616
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This research paper presents a study of undergraduate technology students' self-reflective learning about artificial intelligence (AI). Research on AI literacy proposes that learners must develop five competencies associated with AI: awareness, knowledge, application, evaluation, and development. It is important to understand what, how, and why students learn about AI so formal instruction can better support their learning. We conducted a reflective journal study where students described their interactions with AI each week. Data was collected over six weeks and analyzed using an emergent interpretive process. We found that the participants were aware of AI, expressed opinions on their future use of AI skills, and conveyed conflicted feelings about developing deep AI expertise. They also described ethical concerns with AI use and saw themselves as intermediaries of knowledge for friends and family. We present the implications of this study and propose ideas for future work in this area.
Related papers
- Writing With Machines and Peers: Designing for Critical Engagement with Generative AI [5.719812010814006]
This study proposes a pedagogical design that integrates AI and peer feedback in a graduate-level academic writing activity.<n>Students developed literature review projects through multiple writing and revision stages, receiving feedback from both a custom-built AI reviewer and human peers.
arXiv Detail & Related papers (2025-11-19T02:17:42Z) - Bridging Minds and Machines: Toward an Integration of AI and Cognitive Science [48.38628297686686]
Cognitive Science has profoundly shaped disciplines such as Artificial Intelligence (AI), Philosophy, Psychology, Neuroscience, Linguistics, and Culture.<n>Many breakthroughs in AI trace their roots to cognitive theories, while AI itself has become an indispensable tool for advancing cognitive research.<n>We argue that the future of AI within Cognitive Science lies not only in improving performance but also in constructing systems that deepen our understanding of the human mind.
arXiv Detail & Related papers (2025-08-28T11:26:17Z) - AI Literacy as a Key Driver of User Experience in AI-Powered Assessment: Insights from Socratic Mind [2.0272430076690027]
This study examines how students' AI literacy and prior exposure to AI technologies shape their perceptions of Socratic Mind.<n>Data from 309 undergraduates in Computer Science and Business courses were collected.
arXiv Detail & Related papers (2025-07-29T10:11:24Z) - Immersion for AI: Immersive Learning with Artificial Intelligence [0.0]
This work reflects upon what Immersion can mean from the perspective of an Artificial Intelligence (AI)<n>Applying the lens of immersive learning theory, it seeks to understand whether this new perspective supports ways for AI participation in cognitive ecologies.
arXiv Detail & Related papers (2025-02-05T11:51:02Z) - AI Toolkit: Libraries and Essays for Exploring the Technology and Ethics of AI [0.0]
The AITK project contains both Python libraries and computational essays (Jupyter notebooks)<n>These notebooks have been piloted at multiple institutions in a variety of humanities courses centered on the theme of responsible AI.<n>Pilot studies and usability testing results indicate that AITK is easy to navigate and effective at helping users gain a better understanding of AI.
arXiv Detail & Related papers (2025-01-17T22:08:52Z) - 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.<n>The study emphasizes deliberate strategies to ensure AI complements, not replaces, genuine cognitive effort.
arXiv Detail & Related papers (2024-12-02T14:08:07Z) - Qualitative and quantitative analysis of student's perceptions in the use of generative AI in educational environments [0.0]
The effective integration of generative artificial intelligence in education is a fundamental aspect to prepare future generations.
The objective of this study is to analyze from a quantitative and qualitative point of view the perception of controlled student-IA interaction within the classroom.
arXiv Detail & Related papers (2024-05-22T09:56:05Z) - AI for social science and social science of AI: A Survey [47.5235291525383]
Recent advancements in artificial intelligence have sparked a rethinking of artificial general intelligence possibilities.
The increasing human-like capabilities of AI are also attracting attention in social science research.
arXiv Detail & Related papers (2024-01-22T10:57:09Z) - The Role of AI in Drug Discovery: Challenges, Opportunities, and
Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed.
The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z) - Cybertrust: From Explainable to Actionable and Interpretable AI (AI2) [58.981120701284816]
Actionable and Interpretable AI (AI2) will incorporate explicit quantifications and visualizations of user confidence in AI recommendations.
It will allow examining and testing of AI system predictions to establish a basis for trust in the systems' decision making.
arXiv Detail & Related papers (2022-01-26T18:53:09Z) - Stakeholder Participation in AI: Beyond "Add Diverse Stakeholders and
Stir" [76.44130385507894]
This paper aims to ground what we dub a 'participatory turn' in AI design by synthesizing existing literature on participation and through empirical analysis of its current practices.
Based on our literature synthesis and empirical research, this paper presents a conceptual framework for analyzing participatory approaches to AI design.
arXiv Detail & Related papers (2021-11-01T17:57:04Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - 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)
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.