The Role of AI, Blockchain, Cloud, and Data (ABCD) in Enhancing Learning Assessments of College Students
- URL: http://arxiv.org/abs/2503.05722v1
- Date: Mon, 17 Feb 2025 15:11:44 GMT
- Title: The Role of AI, Blockchain, Cloud, and Data (ABCD) in Enhancing Learning Assessments of College Students
- Authors: Joel Mark P. Rodriguez, Genesis S. Austria, Glen B. Millar,
- Abstract summary: This study investigates how ABCD technologies can improve learning assessments in higher education.<n>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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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. Through a quantitative research design, survey responses were gathered from university students, and statistical tests, such as correlation and regression, were used to establish relationships between Perceived Usefulness (PU), Perceived Ease of Use (PEU), and Behavioral Intention (BI) towards ABCD adoption. The results showed that there was no significant relationship between PU, PEU, and BI, which suggests that students' attitudes, institutional policies, faculty support, and infrastructure matter more in adoption than institutional policies, faculty support, and infrastructure. While students recognize ABCD's efficiency and security benefits, fairness, ease of use, and engagement issues limit their adoption of these technologies. The research adds to Technology Acceptance Model (TAM) and Constructivist Learning Theory (CLT) by emphasizing external drivers of technology adoption. The limitations are based on self-reported data and one institutional sample. It is suggested that universities invest in faculty development, infrastructure, and policy-making to facilitate effective and ethical use of ABCD technologies in higher education.
Related papers
- Form-Substance Discrimination: Concept, Cognition, and Pedagogy [55.2480439325792]
This paper examines form-substance discrimination as an essential learning outcome for curriculum development in higher education.
We propose practical strategies for fostering this ability through curriculum design, assessment practices, and explicit instruction.
arXiv Detail & Related papers (2025-04-01T04:15:56Z) - To Deepfake or Not to Deepfake: Higher Education Stakeholders' Perceptions and Intentions towards Synthetic Media [0.0]
Deepfake technologies use generative artificial intelligence to mimic a person's likeness or voice.<n>This study investigated stakeholder perceptions and intentions regarding deepfakes in higher education.<n>We found that academic stakeholders demonstrated a relatively low intention to adopt these technologies.
arXiv Detail & Related papers (2025-02-25T10:32:19Z) - Auto-assessment of assessment: A conceptual framework towards fulfilling the policy gaps in academic assessment practices [4.770873744131964]
We surveyed 117 academics from three countries (UK, UAE, and Iraq)
We identified that most academics retain positive opinions regarding AI in education.
For the first time, we propose a novel AI framework for autonomously evaluating students' work.
arXiv Detail & Related papers (2024-10-28T15:22:37Z) - Generative AI Adoption in Classroom in Context of Technology Acceptance Model (TAM) and the Innovation Diffusion Theory (IDT) [1.9659095632676098]
This study aims to dissect the underlying factors influencing educators' perceptions and acceptance of GenAI and LLMs.
Our investigation reveals a strong positive correlation between the perceived usefulness of GenAI tools and their acceptance.
The perceived ease of use emerged as a significant factor, though to a lesser extent, influencing acceptance.
arXiv Detail & Related papers (2024-03-29T22:41:51Z) - Bringing Generative AI to Adaptive Learning in Education [58.690250000579496]
We shed light on the intersectional studies of generative AI and adaptive learning.
We argue that this union will contribute significantly to the development of the next-stage learning format in education.
arXiv Detail & Related papers (2024-02-02T23:54:51Z) - Exploring Federated Unlearning: Analysis, Comparison, and Insights [101.64910079905566]
federated unlearning enables the selective removal of data from models trained in federated systems.<n>This paper examines existing federated unlearning approaches, examining their algorithmic efficiency, impact on model accuracy, and effectiveness in preserving privacy.<n>We propose the OpenFederatedUnlearning framework, a unified benchmark for evaluating federated unlearning methods.
arXiv Detail & Related papers (2023-10-30T01:34:33Z) - Artificial Intelligence across Europe: A Study on Awareness, Attitude
and Trust [39.35990066478082]
The aim of the study is to gain a better understanding of people's views and perceptions within the European context.
We design and validate a new questionnaire (PAICE) structured around three dimensions: people's awareness, attitude, and trust.
We highlight implicit contradictions and identify trends that may interfere with the creation of an ecosystem of trust.
arXiv Detail & Related papers (2023-08-19T11:00:32Z) - Learning to Prompt in the Classroom to Understand AI Limits: A pilot
study [35.06607166918901]
Large Language Models (LLM) and the derived chatbots, like ChatGPT, have highly improved the natural language processing capabilities of AI systems.
However, excitement has led to negative sentiments, even as AI methods demonstrate remarkable contributions.
A pilot educational intervention was performed in a high school with 21 students.
arXiv Detail & Related papers (2023-07-04T07:51:37Z) - A Comprehensive AI Policy Education Framework for University Teaching
and Learning [0.0]
This study aims to develop an AI education policy for higher education by examining the perceptions and implications of text generative AI technologies.
Data was collected from 457 students and 180 teachers and staff across various disciplines in Hong Kong universities.
The study proposes an AI Ecological Education Policy Framework to address the multifaceted implications of AI integration in university teaching and learning.
arXiv Detail & Related papers (2023-04-29T15:35:39Z) - AGI: Artificial General Intelligence for Education [41.45039606933712]
This position paper reviews artificial general intelligence (AGI)'s key concepts, capabilities, scope, and potential within future education.
It highlights that AGI can significantly improve intelligent tutoring systems, educational assessment, and evaluation procedures.
The paper emphasizes that AGI's capabilities extend to understanding human emotions and social interactions.
arXiv Detail & Related papers (2023-04-24T22:31:59Z) - The Commodification of Open Educational Resources for Teaching and
Learning by Academics in an Open Distance e-Learning Institution [0.0]
The use of open educational resources (OER) is gaining momentum in higher education institutions.
This study sought to establish academics' perceptions and knowledge of OER for teaching and learning in an open distance e-learning university.
The study found that academics with prior experience and knowledge of OER are more successful in the use of these resources for teaching, learning, and research.
arXiv Detail & Related papers (2021-08-23T05:17:47Z) - 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)
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.