6. Dynamic individualization

Even though a GenAI is not an ITS, as some ICALL systems were, can it consider and appropriately respond to individual learner differences (Dörnyei, 2006)? On the one hand, the limits of appropriate corrective feedback GenAIs can give curtail the possibilities for individualized help learners receive. On the other, the probabilistic, nonlinear approach and other (hidden) traits of LLMs mean that the experience of text generation is unique to each user (Wolfram, 2023, February 14). In other words, the same prompt put in twice will normally generate (at least slightly) different texts. This is a feature of GPTs because they contain slight distortions to make their generated text more human-like. Texts can also be generated using different voices, styles, and registers as well as for different language proficiency and readability levels. Thus, providing an individualized textual experience is a strength of GenAIs that are based on LLMs.

My inspiration for this title came from the book  
Snyder, T. (2017). On tyranny: Twenty lessons from the twentieth century. Tim Duggan Books. 

I am sharing these early drafts of a book chapter I published in 
Yijen Wang, Antonie Alm, & Gilbert Dizon (Eds.) (2025), 
Insights into AI and language teaching and learning. Castledown Publishers. 

https://doi.org/10.29140/9781763711600-02.

With this one, six of the seven lessons have been prepared. Here is a quick list of what I posted in this context before.

– historical introduction 
– Lesson #1: exposure to authentic language
– Lesson #2: communication in context
– Lesson #3: interaction in language learning with GenAI
– Lesson #4: appropriate error correction and contingent feedback
– Lesson #5: Recording learner behavior and student modeling

As discussed above, LLMs are machine-learnt ANNs, which were trained on a very large number of texts from the internet. They did not gain ‘their experience’ with a large group of students who they got to know over the years. This is what teachers do, and this is what student models contribute in an AI system. GPTs are not meant or designed to function as an intelligent tutoring system, because they have little to no information about the individual student, planned instructional sequences, and the curricular context of an activity or lesson. Information about an individual student is stored in a student profile. Virtual learning environments and quiz tools, for example, store scores, time on task, resources accessed, etc. in the system. This structured information can be used in a student model of an intelligent language tutoring system to ‘reason’ about the student’s learning and language beliefs, which then informs the next steps of the system: what feedback is given when, what help is offered, which resources are shown or hidden, which activity is pushed next, … And GenAI’s strength is in the generation of plausible texts and not in the administering of meaningful and effective learning sequences. GenAIs can collect further textual data to further refine their LLM, but they do not (yet) collect learner information like bespoke learning environments and apps do. Thus, the individualization of learning processes – also when employing GenAI tools – is still the remit and responsibility of the teacher and must not be delegated to the machine. GenAI can adapt the generated text to the user’s prompt, but it is not designed to deliver or implement adaptive instruction (Schulze et al., 2025 in press). Adaptation of the machine to the learner was in ICALL because of the student model and pre-programmed feedback algorithms, for example. Also, tutorial CALL software in the early phase of CALL had instructional sequences of its activities hard-wired and had limited capability of adapting to the learning path of individual students and their learning preferences through inbuilt branching between activities, for example based on prior answers in the previous activities or overall score thresholds.

To be continued …

References

Dörnyei, Z. (2006). Individual Differences in Second Language Acquisition. AILA Review, 19, 42-68.

Schulze, M., Caws, C., Hamel, M.-J., & Heift, T. (2025 in press). Adaptive instruction. In G. Stockwell (Ed.), Cambridge Handbook of Technology in Language Teaching and Learning (pp. t.b.d.). Cambridge University Press.

Wolfram, S. (2023, February 14). What is ChatGPT doing … and why does it work? Stephen Wolfram Writings. https://writings.stephenwolfram.com/2023/02/what-is-chatgpt-doing-and-why-does-it-work