AI evidence extraction

Human-AI Collaboration: A Student-Centered Perspective of Generative AI Use in Higher Education

Liana Razmerita · European Conference on e-Learning · 2024

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

7/10

Relevance

0/4

Quality (LMQS)

Evidence

Citations

1.18

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.34190/ecel.23.1.3008

Methodology & findings

Study design

Mixed methods study combining quantitative and qualitative data collection from business school students to explore human-AI collaboration in academic contexts

Main result

The study found that "Students are collaborating with AI using ChatGPT for expanding knowledge on academic themes, summarizing concepts, theories, and generating ideas for research topics and methods." Additionally, "Time saving, enhanced productivity and user-friendliness of the tool were identified as the main benefits associated with Gen AI, whereas the risk of plagiarism, its inaccuracy in responses and the need for prior knowledge were identified as the main drawbacks."

Reports effect sizes.

Research paradigm

Mixed methods (quantitative and qualitative)

Author conclusions

The authors conclude that "While our findings underscore the potential of Gen AI to significantly enhance student learning experiences, they also underscore the importance of exercising caution and awareness of associated risks to automate learning." They also state that "This study seeks to enrich our comprehension of AI's transformative role in higher education, with a specific focus on the student-centered perspective."

Risk of bias

Selection bias: Sample limited to business school students, potentially not representative of broader higher education population; Self-report bias: Student perceptions and attitudes regarding Gen AI usage may be subject to social desirability bias; Selection bias: sample limited to business school students, may not generalize to other disciplines or student populations; Self-report bias: student perceptions may not reflect actual usage patterns; Potential social desirability bias in reporting AI usage and attitudes; Potential selection bias (sample limited to business school students); self-reported usage and perceptions may suffer from social desirability bias; no control group mentioned for comparison; single institution/program context may limit generalizability.

Open questions raised

The paper identifies the need to understand the evolving relationship between humans and AI in performing learning and knowledge-intensive tasks, and calls for investigation into how Gen AI usage affects students' behavior, academic work, and attitudes towards AI in higher education contexts.
The paper addresses how Gen AI usage affects students' behavior, academic work, and attitudes towards AI, but does not explicitly identify future research directions in the abstract provided.
The paper indicates future research should examine: (1) the long-term impacts of Gen AI on student learning outcomes; (2) institutional policies and guidelines for appropriate Gen AI use; (3) the effectiveness of AI-augmented versus AI-automated learning approaches; (4) comparative analysis across different student populations and disciplines.

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