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• ​GenAI uses
• ADDIE model
• Self-determination theory

Theories of Teaching & Learning

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To assist University EAP teachers on developing GenAI-tools-assisted academic English tasks, this teacher guide provides practical tips on how to adapt/modify tasks generated by GenAI tools, introduces theories underpinning EAP task designs, limitations and concerns of AI tools, prompting skills and useful sample tasks for reference. 

GenAI tools can be used for...

Function	Example
Content development	Brainstorming ideas for topics (e.g. skills in giving a persuasive speech), producing resources (e.g. generating dialogues), and outlining ideas.
Corpus search	Retrieval of example sentences using the given vocabulary, word usage, synonyms, grammatical sentence structures, etc.
Feedback and Editing	E​diting and proofreading with explanations. Editing for specific aspects of writing (e.g. organization, language, clarity, articulation of a given sentence), and analysis of errors. Offering personalized feedback on writing and speaking.
Self-regulated learning and Response to language questions	P​roviding answers to specific questions (e.g. explain a formal writing style, collocations, etc.).
Grammar, vocabulary, academic style and mechanism support	Offering explanations of unknown grammar and vocabulary items and how to write formally using an appropriate register and tone.
Paraphrasing and summarizing	Offering paraphrased/summarized versions of passages/paragraphs.

Theories Underpinning Task Design for Academic English Skills

​1. ADDIE model

The Analysis, Design, Development, Implementation and Evaluation or ADDIE model (Branch, 2009) offers a comprehensive and robust approach to designing learning tasks, where the aim is to teach learners to use academic English language effectively for their communicative needs with learning taking place when teaching practices/tasks are meaningful and real to learners (Dörnyei 2013).

​The first stage of the ADDIE model (Branch, 2009) involves analysing learners’ characteristics (e.g. existing knowledge, attitudes) to enable teachers to identify which skills in academic English are more challenging for learners and hence require more coaching. It is also important to collect views from teachers on course demands in university courses (e.g. writing tasks, text types, academic presentation tasks, etc.) and their views on the difficulties perceived by their students in academic English, as well as the challenges they anticipate they will have with using AI (e.g. ChatGPT) created tasks and integrating them into academic English skills courses and content-based courses requiring academic English to address students’ learning needs. 

In the design phase, objectives of the academic English learning tasks have to be clearly specified and learning tasks incorporating the use of GenAI have to be designed and adapted.

Dörnyei’s (2013) principled communicative language teaching approach aims to facilitate learners’ communicative competence. It is based on these principles, namely:

1. Tasks have to be relevant, engaging and meaning focused,
2. Explicit input by teachers is needed,
3. Controlled practice activities are required,
4. Acquisition of formulaic language is needed, and
5. Exposure to input and plenty of opportunities for genuine interaction are needed. 

Thus, according to this approach, the teacher serves as a facilitator by creating an environment conducive to language learning, and learning occurs when learners collaborate with others.

​2. Self-determination Theory (SDT) 

The self-determination theory (SDT) developed by Deci and Ryan (1985) is a widely adopted macro-theory of human motivation in language learning (Du & Alm, 2024). 

According to SDT, there are three psychological needs that are innate to all individual learners. They are:

1. Autonomy
2. Competence
3. Relatedness

Autonomy refers to the psychological need of possessing a sense of control over one’s personal behaviours and decisions (Deci & Ryan, 1985). According to SDT, individuals with high levels of autonomy tend to perform actions that are intrinsically motivated. In EAP learning, the presence of AI tools could potentially increase students' sense of self-control as AI tools offer new self-learning options, allowing students to engage in learning at their own pace.

Competence describes the need of individuals to feel effective in ones’ acts and doings, and to possess the freedom to manifest one’s skills and capabilities (Deci & Ryan, 1985). Deci and Ryan (1985) posit that satisfaction of individuals’ competence needs could motivate them to persist in desirable behaviors (e.g. learning). Thus, students might be benefited by AI assisted task designs, where more opportunities could be offered for students to practice and improve their skills. 

Relatedness pertains to the need of feeling meaningfully connected with others, in a way that is socially supportive and reciprocal (Deci & Ryan, 1985). It is not yet clear whether students could form social bonds and develop a sense of belonging through interactions with AI. Nevertheless, some suggest that students could still be motivated by receiving feedback and positive interactions (Du & Alm, 2024). 

​Thus, based on the findings of related studies, teachers might consider addressing some or all of the psychological needs highlighted by SDT when designing EAP tasks for university students. To give a few examples: ​

1. ​First, when designing EAP activities, teachers could incorporate a short AI literacy section, demonstrating prompting skills that are suitable for the learning activities. For instance, teachers can design an academic summary task, where students evaluate an AI-generated summary of an academic essay.​

2. As a warm-up activity, an example prompt can be introduced to students showcasing how the CREATE framework can be used to help students complete the task, such as instructing AI to create an evaluation rubric for them to follow.

3. Moreover, teachers could ask students to adjust the prompt to meet their personal learning needs, such as telling AI what the students themselves think they struggle the most with when writing academic summaries. This could help students improve their prompting skills and lower the hurdles for self-learning by seeking personalized assistance from AI, thus satisfying their need of autonomy, as well as their overall academic English competence.

4. In terms of satisfying students’ need for relatedness, teachers can consider designing tasks where students interact with both their peers and AI tools for different perspectives. For instance, EAP teachers can design a paraphrasing task where students attempt to paraphrase sentences taken from academic essays, as they learn different academic expressions and the proper ways of citing sources accurately and ethically.

5. Students can be asked to exchange their work with each other for peer evaluation and comments. Teachers can then ask students to seek additional feedback from AI tools to facilitate in-depth discussions and consolidate students’ understanding and language skills. 

In summary, by combining different approaches such as the ADDIE model, SDT, as well as both human and AI-centered activities, teachers will be able to design highly engaging tasks in a structured way.

References (click to expand)

1. Branch, R. M. (2009). Instructional design: The ADDIE approach (1st ed.). Springer-Verlag.
2. Deci, E. and Ryan, R. (1985). Intrinsic motivation and self-determination in human behavior. https://doi.org/10.1007/978-1-4899-2271-7
3. Dörnyei, Z. (2013). Communicative language teaching in the twenty-first century: The ‘Principled Communicative Approach’. In J. Arnold & T. Murphy (Eds.) Meaningful interaction: Earl Stevick’s influence on language teaching (pp. 161-171). Cambridge University Press.
4. Du, J., & Alm, A. (2024). The impact of ChatGPT on English for academic purposes (EAP) students’ language learning experience: A self-determination theory perspective. Education Sciences, 14(7), 726.

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