AI evidence extraction

The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers?

Cecilia Ka Yuk Chan, Katherine K. W. Lee · Smart Learning Environments · 2023

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

7/10

Relevance

3/4

Quality (LMQS)

Evidence

523

Citations

340.21

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1186/s40561-023-00269-3

Methodology & findings

Study design

Mixed methods online survey consisting of both open-ended and closed-ended questions (22 survey items).

Sample

N = 583, 4 groups

Primary method

Descriptive analysis for quantitative data from closed questions; independent samples t-tests (with Welch's t adjustment where Levene's test was significant, p < 0.05) to compare student and teacher responses; thematic coding analysis for qualitative data with intercoder agreement calculated using percentage agreement (72% for willingness/intentions coding, 77% for concerns coding). Missing values were treated as follows: 'Not Sure' responses outside the 5-point Likert scale were treated as missing in main t-tests, then re-analyzed by recoding responses as 0 (within SD-SA scale) or 1 ('Not Sure').

Main result

The study found that "Gen Z participants were generally optimistic about the potential benefits of GenAI, including enhanced productivity, efficiency, and personalized learning, and expressed intentions to use GenAI for various educational purposes." Additionally, "Gen X and Gen Y teachers acknowledged the potential benefits of GenAI but expressed heightened concerns about overreliance, ethical and pedagogical implications, emphasizing the need for proper guidelines and policies to ensure responsible use of the technology."

Reports effect sizes and confidence intervals.

Research paradigm

Mixed methods (quantitative descriptive + qualitative thematic analysis)

Author conclusions

"The adoption of GenAI technologies among Gen X, Gen Y (Millennials), and Gen Z varies due to the unique generational experiences and levels of familiarity with technology among each group." The authors emphasize that "the findings of this study contribute to the growing body of literature on the use of AI in education and provide important insights into the attitudes and intentions of Gen Z students and Gen X and Gen Y teachers towards the adoption of GenAI in educational settings." They further conclude that "combining technology with traditional teaching methods to provide a more effective learning experience" is essential, as "Although Gen Z students are tech natives and have grown up with technology, it does not necessarily mean they prefer a tech-only approach."

Risk of bias

Selection bias: Convenience sampling used rather than random sampling; Geographic bias: 86.8% of participants (506/583) from Hong Kong; Self-reported data: Reliance on self-reported responses may be subject to social desirability bias; Assumption of generational membership: Teachers' generational assignment assumed based on role rather than measured age; Temporal specificity: Data collected in early 2023 during rapid ChatGPT adoption phase; Selection bias: Convenience sampling used due to need for timely data collection; Geographic bias: Majority of participants (506 of 583) from Hong Kong; Social desirability bias: Self-reported data may not reflect true attitudes or behaviors; Age assumption bias: Teachers' generational classification assumed based on role rather than actual age; Sample representativeness: Relatively small sample size limits generalizability; Convenience sampling used, which introduces selection bias; Majority of participants from Hong Kong, limiting geographic diversity; Assumption that teachers are Gen X/Y without verified age data; Self-reported data subject to social desirability bias; Relatively small sample size

Limitations

"This study has several limitations that should be acknowledged
First, the assumption that Gen X and Gen Y participants are teachers may not be entirely accurate
future studies should obtain participants' ages for more precise generational categorization
Second, the majority of the students and teachers in the study were from Hong Kong, which may limit the generalizability of the findings to other cultural and educational contexts
Likewise, generational studies are often geographically dependent, and most of the literature in this area is based on Western populations, potentially limiting the applicability of these findings to other regions
The sample size was also relatively small, further limiting the generalizability of the results." Additionally, "the reliance on self-reported data in this study may be subject to social desirability bias, which could affect the accuracy of participants' responses."

Open questions raised

Lack of precise generational categorization through direct age measurement
Limited research on cultural variations in GenAI adoption across different regions
Need for larger, more representative samples from multiple institutions and countries
Underexplored perspectives of other stakeholders (policymakers, university administrative staff)
Limited longitudinal research on long-term impacts of GenAI integration on teaching and learning outcomes
Need for evidence-based guidelines and policies for GenAI integration in higher education

Data: "The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request."; The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.Code: Not mentionedExtracted from: pdfAgreement 56%

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