EFL Students' Attitudes and Contradictions in a Machine-in-the-loop Activity System
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Abstract (arXiv)
This study applies Activity Theory and investigates the attitudes and contradictions of 67 English as a foreign language (EFL) students from four Hong Kong secondary schools towards machine-in-the-loop writing, where artificial intelligence (AI) suggests ideas during composition. Students answered an open-ended question about their feelings on writing with AI. Results revealed mostly positive attitudes, with some negative or mixed feelings. From a thematic analysis, contradictions or points of tension between students and AI stemmed from AI inadequacies, students' balancing enthusiasm with preference, and their striving for language autonomy. The research highlights the benefits and challenges of implementing machine-in-the-loop writing in EFL classrooms, suggesting educators align activity goals with students' values, language abilities, and AI capabilities to enhance students' activity systems.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 5 of its 36 references that are in the map. First posted on arXiv 13 Jul 2023.
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References · 5
- 2017Attention Is All You Needcited by 59,932
- 2020Language Models are Few-Shot Learnerscited by 32,199
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
- 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
- 2022SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Modelscited by 1,338
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