GenAI in the English subject – from the pupils’ perspective. A mixed method study on pupils’ actual and wanted use of generative artificial intelligence in the English subject

Abstract

This Master’s thesis aims to explore how pupils use generative artificial intelligence (GenAI) in the English subject and how pupils would like to use the technology in the future. This research objective was based on the lack of research into both the pupils’ perspectives on using GenAI, as well as subject-specific research on the use of the technology. The thesis is based on the findings of a mixed-method research project, where a survey was conducted to explore pupils’ use of GenAI in the English subject, and group interviews were conducted to explore their views on the future use of GenAI. The research project was conducted at an age-mixed school in northern Norway in January and February 2024, with 39 participants, of which 18 took part in group interviews.

The findings presented in this thesis show that a majority of the pupils taking part in the survey have used GenAI for tasks in the English subject. Different types of use are also shown, with a majority of the pupils saying they have used GenAI for explanation of difficult material, as a “discussion partner” for inspiration, translation, and to proofread text. The findings also show a difference in the use of GenAI between pupils with different levels of perceived competence in the English subject. A thematic analysis of the interview data was conducted, and the emerging themes of how pupils want to use GenAI in the future are presented. These themes of wanted use mostly mirror the use cases from the survey, namely: inspiration, explanation, help, information, and improving language.

In the discussion, the findings are seen in the context of national and international guidance documents on the use of GenAI in education, as well as previous research on the subject. The pupils’ statements in the group interviews show a high level of insight into the possible dangers of becoming over-reliant on GenAI, and their reflections on the subject are reminiscent of several points highlighted in the different guidance documents. The thesis concludes with a call for further research into the field of GenAI so that educated decisions and policies can be made.