Seven Lessons

There has always been some interaction between AI and language and learning for the last 70 years. In computer-assisted language learning (CALL), people have worked on applying AI – and they called it ICALL – for almost 50 years. For GenAI, what can we learn from these efforts of working with good old-fashioned AI for such a long time?

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.

In conclusion, we will recapitulate and condense the seven lessons that we can learn from ‘good old-fashioned AI’ and ICALL with its declarative knowledge, engineered algorithms, and symbolic NLP and see how they can be applied to GenAI with its machine-learnt complex artificial neural networks.

1. Exposure to rich, authentic language
   GenAI is capable of providing ample exposure to rich language just in time, on the right topic, and at the right level. Generated texts consist of mostly accurate language forms and are plausible, so that they lend themselves to an interpretation in context by the students. This gives such a text an authentic feel. Here GenAI compares very well to the limited linguistic scope of ICALL systems.
2. Communication in context
   GenAI, also because of the comprehensive coverage of the LLMs, can sustain conversations with learners on different topics. Its natural language understanding is such that it can take into consideration prior textual context, making any conversation more natural. This was impossible with ICALL systems and chatbots of the past. However, teachers and students need to be aware that they are communicating with a machine, a stochastic parrot (Bender et al., 2021). This requires informed reflection on a new form of communication and learning, to avoid the anthropomorphizing of machine and its output.
3. Appropriate error correction and contingent feedback
   This is the area where we can learn most from ICALL and tutorial CALL. Especially with giving metalinguistic feedback, GenAI has too many shortcomings. Researchers need to explore how the automatic error correction, which happens frequently, impacts aspects of language learning such as noticing.
4. Varied interaction in language learning tasks
   This is the area where we have many new opportunities to explore, although we can take inspiration particularly from projects in ICALL and game-base language learning. GenAI is most suitable as a partner in conversation and learning.
5. Recording learner behavior and student modeling
   Student modeling has a long tradition – not just in ICALL – in AI and education. GenAI tools by themselves are that – tools and not tutors. They can be embedded in other learning systems, but they cannot be used as virtual tutors, because their information about learners and the learning context are serendipitous at best.
6. Dynamic individualization
   GenAI provides teachers and students with an individual experience with generated texts of high quality.  The adaptive instruction (Schulze et al., 2025 in press), however, which has been an ambition of ICALL research, has not yet been achieved. Broader research and development in AI, beyond GenAI, is still necessary to achieve dynamic individualization in what can truly be termed ICALL.
7. Gradual release of responsibility
   Since the instructional sequences, pedagogical approaches, and teaching methods are not present in GenAI, teachers need to design the use of GenAI as one of the tools in the learning process carefully. Teachers must not render the control of curricular and pedagogical decisions about activity design, learning goals, lesson contents, and learning materials to the machine.

GenAI, due to its powerful LLMs, has lifted AI in language education to a new quality. Such a disruptive technology shows great promise, provides many additional opportunities, and poses some challenges for teachers, students, and researchers alike.