Plenary session by Patricia AngoyEnglish language – the coloniser: A black female leader’s response
In today’s plenary session, Patricia Angoy shares her decade-long experiences of working in international or transnational education (TNE) as a teacher and educational leader. She begins by inviting the audience to imagine what it would have felt like to speak or listen to an extinct language, or a language that was never influenced by English in any form. Her position is that culture is embedded in language expressions.
She further illustrates her point with an example of somebody who rejects their first or local language and who has adopted a majority language in their own country. Whether this is by force or by conscious choice, she highlights the power that the English language holds in social and educational settings. English has become the preferred language among many people due to its status and the advantages that speakers enjoy. This is sometimes at the expense of the speaker’s original or ancestral first language, which is at best relegated to a secondary status or at worst despised for being unrefined.
She questions the entrenched ideas of one’s English language knowledge as a measure of their intelligence or social status. She invites teachers and educational leaders to examine their beliefs about the English language by considering Sharon Stein’s questions:
- What illusions have shaped me?
- What performances have I confused with responsibilities?
- If we let go of Empire’s stories, what else might we make space for?
She later poses several more guiding questions:
- How can we avoid the coloniality of language when English is the coloniser’s language?
- How do we teach through the language, about the language, and with the language so that we open new worlds and not shut them down?
Mentioning her journey of reconnecting with the language that her Caribbean ancestors once spoke, she suggests that a person becomes disconnected from their original culture when they willingly or unwillingly adopt English as their preferred language. English language teachers should, therefore, be aware of this attitude when working with learners of different nationalities in TNE. It echoes the current debates about Anglo-centricism in transnational education models.
Finally, she cautions against how multilingualism is being interpreted and promoted in TNE. She expresses her concerns over the current power structures by highlighting two opposite kinds of perception of a bilingual person or polyglot. While there are generally positive views on monolingual people’s ability to speak a foreign language in their own country, immigrants are often perceived negatively for their limited ability to speak the language of their host country.
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Workshop by Peter Lucantoni and Emir Aydin
Fostering learner autonomy through collaboration in AI
Peter Lucantoni and Emir Aydin (Cambridge University Press Türkiye) begin their workshop by contrasting control with autonomy. Learner autonomy refers to the ability to exercise control in the learning process. It means that learners maintain agency while working with other people and technological tools.
Peter and Emir then present the three factors for consideration when designing agentic artificial intelligence (AI) tools for teaching and learning: role, rules, and limits. (Teachers can use elevenlabs.io to build or train an agentic AI chatbot.) Using a specially trained AI chatbot with various built-in agents and parameters controlled by the teacher, they suggest that learners can use agentic AI as a partner in doing projects.
In their subsequent demonstration of agentic AI, Peter and Emir’s chatbot responds to any prompt request to produce a written text with only closed (yes/no) follow-up questions and suggested ideas. They reveal how they have trained their chatbot:
- Role: “Act as my partner in the project (attached photo). Your role is ...”
- Rules: “Do not write the text. I will write it.”
- Limits: “Respond with ...”
As the chatbot has been trained not to spoon-feed direct answers, it will not provide the learners with a ready-made text despite their repeated prompt requests. To a certain extent, this requires the learners to engage in a minimal amount of critical thinking.
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Forum on feedback by AI versus teachers
Selda Gumus (Ozyegin University, Türkiye) outlines the case for the learners to use AI for feedback on their work. She suggests that AI tools can help teachers of large classes better deal with mixed abilities, thereby ensuring equal opportunities for each learner to achieve the learning outcomes.
She introduces different procedures for in-class training:
- Reading skills: learners can work on their reading comprehension skills by using a specially trained AI chatbot [link to Peter Lucantoni and Emir Aydin’s workshop] of the teacher’s choice. The chatbot asks comprehension questions and helps the learners to reach the intended responses.
- Writing skills: learners can seek support from AI tools in the drafting process, rather than ask AI chatbots to produce a ready-made text.
- Critical thinking: learners need to be made aware of how AI conversation works. They should not stop engaging with an AI chatbot after receiving a single response to their questions. What they should do is engage with the response critically by asking follow-up questions.
In addition to task output (product), teachers can use other assessment tools such as the history of learners’ interactions with AI chatbots and reflections on strategic awareness or perceived usefulness of AI (process).
Anna Poghosyan (American University of Armenia) reports on her research project. It involves a case study of graduate students’ engagement with both human and AI feedback on writing skills. In her case study, the participants first received feedback from the teacher, who commented on broad areas such as clarity and coherence. Taking on board the teacher’s feedback, the participants make use of AI tools such as Grammarly for further feedback.
According to her questionnaire results, more than half of the respondents consider both feedback from the teacher and from Grammarly (analytic AI) most engaging. She concludes that AI-generated feedback tends to require lower-order thinking, whereas teachers can give feedback that requires higher-order thinking. Nevertheless, she states that AI literacy is a prerequisite for effective AI-human partnership in feedback giving.
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Talk by Crayton Walker
Teaching modality: A corpus-informed approach
Dr Crayton Walker (University of Birmingham) continues on the theme of using corpus data to inform our choice of language targets in teaching and in textbooks. While he focused on phrases and chunks at last year’s IATEFL conference, he explores modality in his talk this year.
Having established modality as a linguistic means of expressing our attitudes, he shows us groups of sentences taken from the corpus to illustrate the concept of harmony or concurrency, i.e. certain modal verbs tend to be used with specific words in real life. Some examples of this include might seem, must always, can possibly, and must surely.
Although modal verbs are polysemous, he argues that their meaning is not only constructed by the verb itself but also by harmony or concurrency. For example, learners often focus on the context when they learn the meaning of must, such as an obligation or deduction. The missing piece of the puzzle is that teachers and learners often overlook the adverb always in must always, which expresses an obligation, or the word surely in must surely, which refers to a deduction.
He draws our attention to some longer patterns or formulaic language in which modal verbs are used, such as “personal pronoun + must surely + slot + that-clause”. Other examples of patterns include it can possibly be, it should be a matter of, Can I just ask …?, and Could you possibly just ...? In other words, there is a pattern grammar in operation with modality.
As a side remark, he mentions different types of modality, including epistemic (e.g. certainty or belief) and deontic (e.g. obligation, permission and prohibition). He also points out that modality can also be expressed by quasi-modal phrases.
His final and most important message is that sentences in textbook exercises ought to reflect how modality is used in real life. The language should be informed by the examples from a corpus to avoid giving the learners exposure to unnatural models.
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Talk by Ian Pemberton
Using academic viewing circles to teach language and skills
Ian Pemberton (University of Warwick) reflects on one of the questions that he asked himself when he was transitioning from EFL into EAP teaching. His question concerns how language learning can be better promoted in the classroom. This has led him to look into usage-based theories.
Usage-based theories have influenced his development of Academic Viewing Circles. He then mentions two specific sources of influence:
- Construction grammars, which are the antecedents of the Lexical Approach [i.e. a lexicogrammar continuum in which chunks are seen as the interaction between form and meaning]
- Complexity theory, i.e. the emergence of regular patterns among concrete examples of language
Another influence comes from evolutionary biology, which describes the order of acquiring or learning language skills. He suggests that language learning can be enhanced by making use of the learners’ “biological” skills (i.e. listening and speaking) as these represent the first to be acquired among children.
Having addressed his theoretical influences, he outlines the process of developing his model for teaching with videos: Academic Viewing Circles. It began life as his application of Tyson Seburn’s Academic Reading Circles (2016) in EAP classes. [This requires prior knowledge of Seburn’s model for teaching academic reading skills.] Subsequently, Ian Pemberton experimented with different texts while keeping to the same group roles.
When he eventually replaced reading texts with videos, he applied the above usage-based theories to teaching language skills, i.e. using listening and speaking tasks to enhance his students’ learning of various chunks and patterns from the videos. Nevertheless, he decided to do away with the separate roles (e.g. contextualiser) as there was the risk of his students going off-topic or getting distracted from the content and language of the videos.
He outlines the procedure for his Academic Viewing Circles:
- Viewing: video tasks
- Recount: consolidation of new language (chunks and patterns) and peer teaching
- Task: information transfer or synthesis of ideas with collaborative group presentations
Finally, he suggests a range of possible learning targets with videos, using his Academic Viewing Circles. These include triangulation in research skills, paraphrasing, summarising, and academic writing styles, among other examples.
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Talk by Mark Smith
Academic literacies and AI: Teachers giving guidance and fostering agency
Mark Smith (Wimbledon School of English, London) begins by stating his preference for the term “academic literacies” over “study skills”, whose connotation he thinks is based on a deficit model.
Using authentic writing as an example, he states that it is beneficial for teachers to identify the cognitive processes involved, such as interpreting meaning, constructing arguments, connecting ideas, and evaluating ideas. AI tools should be used in a way that helps the learners engage with meaning, rather than one that allows them to conveniently outsource their cognitive processes.
He suggests ways of integrating AI use into three kinds of classroom routines without sacrificing the learners’ cognitive engagement:
Lexical categorisation
- Each learner groups a set of lexical items according to the criteria of their free choice.
- Learners compare and evaluate each other’s criteria, justifying their decisions.
- Learners use AI tools to group the same set of lexical items. They deduce the criteria that their AI tool has used. They also make comparisons between AI and human versions.
Dictogloss
- The whole class follows the usual routine of Dictogloss.
- Learners use AI tools to fill any gaps in their individual reconstructed texts.
- Learners compare and critically analyse the language in both AI and human versions.
Jigsaw reading
- The whole class is divided into two groups. Each group is given a different part of the source text to summarise. At the same time, the summary of each part of the text is generated by an AI chatbot.
- Learners in each group compare both AI and human versions of the summary.
- When they are satisfied with their summaries, groups A and B work together to exchange information and construct the meaning of the whole text.