AI evidence extraction

Exploring students’ perspectives on Generative AI-assisted academic writing

Jinhee Kim, Seongryeong Yu, Rita Detrick, Na Li · Education and Information Technologies · 2024

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

9/10

Relevance

0/4

Quality (LMQS)

Evidence

298

Citations

30.21

FWCI

Top 10%

Impact

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.1007/s10639-024-12878-7

Methodology & findings

Study design

Qualitative case study using semi-structured in-depth interviews (60-90 minutes each) with 20 Chinese students in higher education.

Main result

The study found that students expected AI to serve multiple roles, including "multi-tasking writing assistant, virtual tutor, and digital peer to support multifaceted writing processes and performance." Additionally, "Students perceived that GenAI-assisted writing could benefit them in three areas including the writing process, performance, and their affective domain. Meanwhile, they also identified AI-related, student-related, and task-related challenges that were experienced during the GenAI-assisted writing activity."

Research paradigm

Interpretivist/constructivist qualitative research

Author conclusions

The authors conclude: "This study advances an in-depth understanding of students' perception, expectations, and barriers related to using GenAI for writing tasks while additionally presenting implications for enhancing GenAI-assisted instruction and the design of educational AI." Furthermore, they state that "Rather, they echo existing research that emphasizes the importance of designing human-centered AI in education that prioritizes the needs, characteristics, and experiences of human users, such as students and teachers. This entails designing learning experiences that enhances learners' performance by leveraging AI to amplify the complementary strengths of humans and AI, thereby promoting active collaboration through human-AI interactions in an educational context."

Risk of bias

Selection bias: Purposeful and snowball sampling may not represent broader student populations; Context bias: All participants from a single Sino-British international university; Temporal bias: Single-time interaction with the system may not capture evolving perceptions; Language bias: Interviews conducted in either Mandarin Chinese or English; translation dependency; Researcher bias: Two independent researchers coded data, but potential for interpretive bias in thematic analysis; Small sample size (N=20) limits generalizability; Single-institution sampling from international Sino-British university may not represent broader student populations; One-time interaction with newly developed system may not capture longitudinal perspective changes; Self-selection bias in participant recruitment using purposeful and snowball sampling; Interviewer bias potential in semi-structured interviews conducted by research team members; Language bias: interviews conducted in either Mandarin or English at participant choice; translation from Chinese to English introduces potential interpretation variability despite back-translation verification; Social desirability bias: participants aware they are providing feedback on research team's developed system; Single institution context (Sino-British international university) may not represent broader student populations; One-time interaction with system may not capture longitudinal changes in perception; Researcher-developed system could introduce design bias; Self-selection in purposeful and snowball sampling may introduce selection bias; Language of interview choice (Mandarin or English) could affect response quality

Limitations

The authors state: "although the current study considered students' different characteristics such as majors, AI literacy levels, writing skills, and gender to explore diverse perceptions of GenAI-assisted writing, the sample of 20 Chinese students is not large enough to fully reflect students' views." Additionally, "the interviews were carried out after students' one-time interactions with the system developed by the research team, which may not fully reveal different perceptions as interactions with the system change over evolutionary timescales." Furthermore, "the specific context and curriculum of a Sino-British university, along with the students' diverse educational backgrounds, may influence their perspectives and experiences."

Open questions raised

Future studies should be conducted with quantitative research methods involving larger numbers of students across different learning tasks (argumentative discussions, creative writing) and diverse student characteristics in different learning environments and cultures
Longitudinal designs in actual classroom settings are needed to explore students' perceptions at different time periods of GenAI-assisted writing
Development of comprehensive theoretical and conceptual foundations for human-centered AI in learning by integrating cognitive or behaviorism learning theories
Development of frameworks that elucidate student-AI interaction patterns and inform the design of educational AI systems aligned with educational objectives
Further research on interdisciplinary curricula incorporating prompt engineering across disciplines with integration of technical, contextual, and ethical components
Limited research on GenAI-assisted writing from ESL student perspectives; most existing studies use close-ended survey items that do not uncover specifics about what aspects of technology are most effective or challenging

Extracted from: pdfAgreement 73%

Explore related topics

AI Manuscript Writing Academic Writing Qualitative