The ethical challenges of AI in education

Billie Jago
A group of students stood around a teacher on a laptop
Reading time: 5 minutes

AI is revolutionising every industry, and language learning is no exception. AI tools can provide students with unprecedented access to things like real-time feedback, instant translation and AI-generated texts, to name but a few.

AI can be highly beneficial to language education by enhancing our students’ process of learning, rather than simply being used by students to ‘demonstrate’ a product of learning. However, this is easier said than done, and given that AI is an innovative tool in the classroom, it is crucial that educators help students to maintain authenticity in their work and prevent AI-assisted ‘cheating’. With this in mind, striking a balance between AI integration and academic integrity is critical.

How AI impacts language learning

Generative AI tools such as ChatGPT and Gemini have made it easier than ever for students to refine and develop their writing. However, these tools also raise concerns about whether submitted texts are student-produced, and if so, to what extent. If students rely on text generation tools instead of their own skills, our understanding of our students’ abilities may not reflect their true proficiency.

Another issue is that if students continue to use AI for a skill they are capable of doing on their own, they’re likely to eventually lose that skill or become significantly worse at it.

These points create a significant ethical dilemma:

  • How does AI support learning, or does it (have the potential to) replace the learning process?
  • How can educators differentiate between genuine student ability and AI-assisted responses?

AI-integration strategies

There are many ways in which educators can integrate AI responsibly, while encouraging our learners to do so too.

1.ÌýRedesign tasks to make them more ‘AI-resistant’

No task can be completely ‘AI-resistant’, but there are ways in which teachers can adapt coursebook tasks or take inspiration from activities in order to make them less susceptible to being completed using AI.

For example:

  • Adapt writing tasks to be hyperlocal or context-specific. Generative AI is less likely to be able to generate texts that are context-bound. Focus on local issues and developments, as well as school or classroom-related topics. A great example is having students write a report on current facilities in their classroom and suggestions for improving the learning environment.
  • Focus on the process of writing rather than the final product. Have students use mind maps to make plans for their writing, have them highlight notes from this that they use in their text and then reflect on the steps they took once they’ve written their piece.
  • Use multimodal learning. Begin a writing task with a class survey, debate or discussion, then have students write up their findings into a report, essay, article or other task type.
  • Design tasks with skill-building at the core. Have students use their critical thinking skills to analyse what AI produces, creatively adapt its output and problem solve by fact-checking AI-generated text.

2.ÌýUse AI so that students understand you know how to use it

Depending on the policies in your institution, if you can use AI in the classroom with your students, they will see that you know about different AI tools and their output. A useful idea is to generate a text as a class, and have students critically analyse the AI-generated text. What do they think was done well? What could be improved? What would they have done differently?

You can also discuss the ethical implications of AI in education (and other industries) with your students, to understand their view on it and better see in what situations they might see AI as a help or a hindrance.

3.ÌýUse the GSE Learning Objectives to build confidence in language abilities

Sometimes, students might turn to AI if they don’t know where to start with a task or lack confidence in their language abilities. With this in mind, it’s important to help your students understand where their language abilities are and what they’re working towards, with tangible evidence of learning. This is where the GSE Learning Objectives can help.

The Global Scale of English (GSE) provides detailed, skill-specific objectives at every proficiency level, from 10 to 90. These can be used to break down complex skills into achievable steps, allowing students to see exactly what they need to do to improve their language abilities at a granular level.

  • Start by sharing the GSE Learning Objectives with students at the start of class to ensure they know what the expectations and language goals are for the lesson. At the end of the lesson, you can then have students reflect on their learning and find evidence of their achievement through their in-class work and what they’ve produced or demonstrated.
  • Set short-term GSE Learning Objectives for the four key skills – speaking, listening, reading and writing. That way, students will know what they’re working towards and have a clear idea of their language progression.

4.ÌýDesign tasks that are not AI-dependent

While AI can generate full essays or summaries in seconds, it’s far less effective as a shortcut for productive skills like speaking, especially when tasks are spontaneous, interactive and happen in real time. This makes speaking one of the best areas to focus on for genuine language production in the classroom (without the use of AI).

To reflect real-world communication, we should focus on designing tasks that encourage active listening, responding to others, justifying their opinions and adapting their ideas as the conversation evolved – none of which AI can do for them.

That being said, AI can be helpful for speaking preparation tasks. If students know they have a speaking class or discussion task coming up in their lesson, you could guide them towards using AI to give them some ideas for how to link their points, generate useful and functional phrases around a certain topic, or generate arguments to personalise and adapt. In this way, AI becomes a ‘rehearsal partner’ rather than something students rely on.

By designing tasks that are unscripted, authentic and collaborative, we shift language production into real-time, boosting confidence, building fluency and helping students to develop their speaking skills authentically.

The path forward

AI is here to stay, and its capabilities will only improve from where they are now. This will inevitably give rise to more and more ethical considerations as time goes on. With that in mind, educators and institutions should begin to shape its role in language learning and understand that the key question should not be ‘Should AI be allowed?’ but rather, ‘How can AI be used responsibly to enhance learning, while ensuring a true reflection of student ability'?

Read our blog posts 'How to assess your learners using the GSE Assessment Frameworks' and 'Writing your own English language materials with the GSE'

Learn more about the GSE

About the author

is an ELT writer and teacher trainer specialising in digital learning and assessment. She has written for various ɫèAV titles including Gold Experience, Roadmap, Rise and Shine and PTE Expert, and is a regular item writer for the PTE-Academic exam. Alongside materials writing, she delivers international teacher training sessions and workshops and is the founder of the professional development platform and providing AI expertise to educators.

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