AI evidence extraction

ChatGPT in education: A blessing or a curse? A qualitative study exploring early adopters’ utilization and perceptions

Reza Hadi Mogavi, Chao Deng, Justin Juho Kim, Pengyuan Zhou, Young D. Kwon, Ahmed Hosny Saleh Metwally et al. · Computers in Human Behavior Artificial Humans · 2023

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

8/10

Relevance

0/4

Quality (LMQS)

Evidence

339

Citations

12.15

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1016/j.chbah.2023.100027

Methodology & findings

Study design

Qualitative content analysis across four key social media platforms to understand user experience (UX) and views of early adopters of ChatGPT

Primary method

Qualitative content analysis methodology. Specific software tools and statistical procedures are not mentioned in the abstract.

Main result

The study found that "ChatGPT is most commonly used in the domains of higher education, K-12 education, and practical skills training" and that "the topics most frequently associated with ChatGPT are productivity, efficiency, and ethics." Early adopters hold dichotomous views: some perceive ChatGPT as "a transformative tool capable of amplifying student self-efficacy and learning motivation," while others "worry about a potential overdependence on the AI system, which they fear might encourage superficial learning habits and erode students' social and critical thinking skills."

Reports effect sizes.

Research paradigm

Interpretivism/Qualitative

Author conclusions

The authors conclude that "This dichotomy of opinions underscores the complexity of Human-AI Interaction in educational contexts" and that their "investigation adds depth to this ongoing discourse, providing crowd-sourced insights for educators and learners who are considering incorporating ChatGPT or similar generative AI tools into their pedagogical strategies."

Risk of bias

Selection bias: Sample limited to early adopters on social media platforms, not representative of all educators or learners; Platform bias: Data collected from only four social media platforms, which may skew towards certain demographics and perspectives; Self-selection bias: Early adopters on social media may have stronger opinions (positive or negative) than general population; Analysis bias: Qualitative content analysis subject to researcher interpretation and coding decisions; Selection bias: Early adopters on social media may not represent broader user populations; Platform bias: Analysis limited to four social media platforms with potentially skewed user demographics; Self-selection bias: Users sharing opinions on social media may have stronger views (positive or negative) than non-participants; Temporal bias: Study captures snapshot of early adoption period, views may have evolved; Selection bias: Self-selection of early adopters on social media platforms may not represent broader educational populations; Platform bias: Analysis limited to four social media platforms, which may skew toward certain demographic groups; Social desirability bias: Social media users may present curated perspectives rather than authentic experiences

Open questions raised

The authors identify the need for deeper understanding of user experiences and perceptions of ChatGPT in educational contexts to inform the development of ethically sound AI-integrated learning landscapes. They suggest future research should continue exploring how educators and learners can effectively incorporate generative AI tools into pedagogical strategies.
The authors identify the need for critical exploration of perceptions and experiences of users in AI-integrated learning landscapes to "foster the development of pedagogically potent and ethically sound AI-integrated learning landscapes."

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