Translation and AI: Separated by a common language
In the interaction with a chatbot, one can change the language or prompt the machine to reply in another language than that of the prompt or request a translation of a text generated previously. It is therefore not surprising that dedicated machine translation (MT), such as Google…
Seven principles of Sustained Integrated Professional Development (GenAI-SIPD)
This is part of a draft of an article I wrote with Phil Hubbard. He was the main writer of this part. In this paper, we are proposing a way in which teachers can organize their own professional development (PD) in the context of the rapid expansion…
Generative AI and the Future of Language Classrooms
Over the past decades, those of us interested in computer-assisted language learning have repeatedly seen new technologies arrive with promises to transform language education. From early interactive grammar exercises to multimedia CD-ROMs, from learning management systems to mobile apps, each sparked both excitement and trepidation. Generative artificial…
Language Learning and AI: 7 lessons from 70 years (conclusion)
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…
Language Learning and AI: 7 lessons from 70 years (#7)
7. Gradual release of responsibility Instructional sequences and other learning processes are structured according to pedagogical guidelines and principles and specific teaching methods. For reasons of brevity, we chose one commonly employed method – the gradual release of responsibility (Fisher & Frey, 2021). In an instructional sequence, the…
Language Learning and AI: 7 lessons from 70 years (#6)
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…
Language Learning and AI: 7 lessons from 70 years (#5)
5. Recording learner behavior and student modeling The intelligent tutoring systems in ICALL had this knowledge stored in a student model (Schulze, 2012). Student modeling (e.g., Bull, 1993; Bull, 1994, 2000; Mabbott & Bull, 2004; McCalla, 1992; Michaud & McCoy, 2000; Schulze, 2008; Self, 1974; Tsiriga &…
Language Learning and AI: 7 lessons from 70 years (#4)
4. Appropriate error correction and contingent feedback Rather than focusing on engaging the learner in communicative interaction, learning with ICALL systems was often based on the assumption that corrective feedback on learner language is of great importance. ICALL research particularly in the 1990s and early 2000s focused…
Language Learning and AI: 7 lessons from 70 years (#3)
3. Varied interaction in language-learning tasks The human-machine conversation often works because we are used to adhere – even if the machine cannot and is not — to Grice’s four maxims of conversation (Grice, 1975): quantity (be informative), quality (be truthful), relation (be relevant), and manner (be clear).…
Language Learning and AI: 7 lessons from 70 years (#2)
2. Communication in context Oxford (1993) desired that “communicative competence must be the cornerstone of ICALL” (p. 174), noting that many ICALL projects of her time did not meet that goal, although communication and by extension communicative language teaching have been central ideas in applied linguistics for…
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