Figure 1: Part of an HSPG (linguistic) sign
After outlining the qualitative research method of narrative autoethnography and introducing the research data I used, it is time to begin the story. An autoethnographic narrative can follow a chronological or episodic structure. Since each of the three parts of my narrative are more episodic, in that they address themes that are important for the intersection of AI and language education and relevant for teacher education, the overarching chronology is presented here:
In his book Age of Spiritual Machines, Kurzweil (1999) made predictions for every ten years starting with 2009. Kurzweil, who is best known for his concept of singularity in the context of AI (Kurzweil, 2005), gave a keynote at the Eurocall 2000 conference in Aberdeen, Scotland. At this conference, I led the founding of the ICALL (see below) special interest group in Eurocall.
This is the story of Mathias,
- who started learning Russian in school in East Germany at the age of 9,
- who taught his first training classes to recruits at 19 during his national service, went to university thereafter to become a language teacher and taught German and Applied Linguistics, including CALL, at universities in England, Canada, Germany, Russia, and the US,
- who got interested CALL before he was 29 and at the time of DOS (Wikipedia Contributors, 2026a) and Windows 3.1 (Wikipedia Contributors, 2026b),
- wo completed his PhD (Schulze, 2001) with a research-prototype of a grammar checker for students of German after learning about symbolic natural language processing (NLP) with Head-driven Phrase Structure Grammar (Pollard & Sag, 1994) – HPSG (Figure 1) – before he was 39,
- who co-wrote a book (Heift & Schulze, 2007) and contributed articles on AI and CALL, which was labelled Intelligent CALL (ICALL), to several encyclopedias and handbooks, including one chapter when he turned 49 (Schulze & Heift, 2012) and
- who began to research the intersection of GenAI and language education at the age of 59 (Schulze 2025a, b, c), when he also learned about the tokens (Figure 2), character strings, used in the machine learning for large language models (LLMs) (Schulze, 2025).
CALL = Computer-assisted Language Learning – a field of research, development, and praxis within the larger fields of Applied Linguistics and Language Education.
This story – my narrative data – will be in three parts, following my biography as a language learner, language teacher, and as CALL practitioner and researcher.
Figure 2: Tokens for a Large Language Model
References
Heift, T., & Schulze, M. (2007). Errors and Intelligence in CALL. Parsers and Pedagogues. Routledge.
Kurzweil, R. (1999). The Age of Spiritual Machines. Viking Press.
Kurzweil, R. (2005). The Singularity is Near: When Humans Transcend Biology. Viking.
Pollard, C., & Sag, I. (1994). Head-Driven Phrase Structure Grammar. The University of Chicago Press.
Schulze, M. (2001). Textana – Grammar and Grammar Checking in Parser-Based CALL [PhD Thesis, UMIST]. Manchester.
Schulze, M. (2025). ICALL and AI: Seven lessons from seventy years. In Y. Wang, A. Alm, & G. Dizon (Eds.), Insights into AI and language teaching and learning (pp. 11–31). Castedown Publishers.
Schulze, M., & Heift, T. (2012). Intelligent CALL. In M. Thomas, H. Reinders, & M. Warschauer (Eds.), Contemporary Computer-Assisted Language Learning (pp. 249–265). Bloomsbury.
Wikipedia Contributors. (2026a). Disk Operating System (DOS). Retrieved 2026-05-25 from https://en.wikipedia.org/wiki/Disk_operating_system
Wikipedia Contributors. (2026b). Microsoft Windows. Retrieved 2025-05-25 from https://en.wikipedia.org/wiki/Microsoft_Windows
