SIPD – Panta Rhei Enterprise

Professional development and GenAI

The degree to which generative artificial intelligence (GenAI) has rapidly infiltrated education is unparalleled. Language education has been particularly impacted because GenAI tools process and generate the learning objective of that education, i.e., human language. Language teacher education programs have been faced with addressing GenAI since the public release of ChatGPT in November 2022, and we anticipate that many recent and future graduates will have had some formal education that includes it. Moorhouse & Kohnke (2024) provide initial insights from a group of language teacher educators on this topic. But what about those who have already completed their formal education and are in the language teaching workforce, the millions of individuals across the world actively teaching languages at all levels?

This is part of a draft of an article I wrote with Phil Hubbard. He wrote 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 of Generative AI.
We call this PD sustained integrated PD (GenAI-SIPD). Sustained because it is continuous and respectful of the other responsibilities and commitments teachers have; integrated because the PD activities are an integral part of what teachers do anyway; the teacher retains control of the PD process.

The full article is available as open access:
Hubbard, Philip and Mathias Schulze (2025) AI and the future of language teaching – Motivating sustained integrated professional development (SIPD). International Journal of Computer Assisted Language Learning and Teaching 15.1., 1–17. DOI:10.4018/IJCALLT.378304 https://www.igi-global.com/gateway/article/full-text-html/378304

UNESCO (2024) has recognized the immediate need for AI competency across the board in education and why it should be addressed.

Al can pose significant risks to students, the teaching community, education systems and society at large…In education, Al can reduce teaching and learning processes to calculations and automated tasks in ways that devalue the role and influence of teachers and weaken their relationships with learners. It can narrow education to only that which Al can process, model and deliver. Finally, it can also exacerbate the worldwide shortage of qualified teachers through disproportionate spending on technology at the expense of investment in human capacity development (p. 13).

So, given this litany of dangers, what do we think language teachers need to know and be able to do to achieve a functional level of expertise so that they can safely leverage the affordances of GenAI to improve rather than degrade language learning processes and outcomes? How can language teachers and language programs support them in accomplishing this goal?

In our position paper, we address these questions by focusing on the importance of understanding the fundamentals of AI and its subset GenAI, as recognized in several AI competency and literacy frameworks. For example, the UNESCO (2024) AI Competency Framework for Teachers states that at the lowest of their three levels of AI competency, “Teachers are expected to acquire basic conceptual knowledge on AI, including: the definition of AI, basic knowledge of how AI models are trained, and associated knowledge on data and algorithms” (p. 30). The Educause (2024) Durable AI Literacy Framework, targeted at tertiary institutions, goes further: “Faculty must grasp the core principles of AI, including machine learning, natural language processing, and neural networks. This foundational knowledge is crucial for understanding how AI operates and what its potential applications are in various academic disciplines.” Other frameworks we discuss, such as those from the International Society for Technology in Education (ISTE) and Paradox Learning, echo this need for teacher understanding.

References

Educause. (2024). AI literacy in teaching and learning: A durable framework for higher education. https://www.educause.edu/content/2024/ai-literacy-in-teaching-and-learning/faculty-altl

Moorhouse, B. L., & Kohnke, L. (2024). The effects of generative AI on initial language teacher education: The perceptions of teacher educators. System, 122, 103290

UNESCO. (2024). AI competency framework for teachers. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000391104

What teachers need to know now about GenAI

We begin by reviewing four frameworks covering teacher competencies for AI and GenAI in education as a whole – UNESCO (2024), Educause (2024), ISTE (2024), and Paradox Learning (2023, 2025).

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 of Generative AI.
We call this PD sustained integrated PD (GenAI-SIPD). Sustained because it is continuous and respectful of the other responsibilities and commitments teachers have; integrated because the PD activities are an integral part of what teachers do anyway; the teacher retains control of the PD process.

UNESCO (2024). The UNESCO AI Competency Framework for Teachers begins with a chapter motivating the need for AI skills and knowledge and showing alignment with the more general UNESCO ICT Competency for Teachers Framework. The document covers six key principles, the organizational structure of the framework, and specifications for the three levels of teacher competency. The six key principles are based on humanistic considerations: 1) ensuring inclusive digital futures, 2) a human-centered approach to AI, 3) protecting teachers’ rights and redefining roles, 4) promoting trustworthy and environmentally sustainable AI, 5) ensuring applicability to all teachers, and 6) lifelong professional learning for teachers. The document also includes suggested implementation strategies and highlights the importance of adjusting the framework to fit local contexts.

The first of the three levels of teacher competency is Acquire. UNESCO states “The overall curricular goal in the ‘Acquire’ level is to support all teachers to reach a basic level of AI competency or literacy required by the teaching profession across varied contexts” (p. 28). Regarding the second level, Deepen, “The overall curricular goal in the ‘Deepen’ level is to support teachers to become fully competent teachers or master teachers in using AI” (p. 33). And for the third level, Create, “The curricular goal at the ‘Create’ level is to empower teachers who have sound AI knowledge and competency to become expert teachers and agents of change” (p. 38).

Educause (2024). Educause, an association of over 2,100 member organizations, describe themselves as ”a nonprofit association whose mission is to lead the way, advancing the strategic use of technology and data to further the promise of higher education” (https://www.educause.edu/about). They have produced what they call a “durable framework” for AI literacy in teaching and learning, with separate competencies for students, faculty, and staff. The faculty competencies are divided into technical understanding, evaluative skills, practical application, and ethical considerations.

The technical understanding competency begins with Fundamentals of AI: “Faculty must grasp the core principles of AI, including machine learning, natural language processing, and neural networks. This foundational knowledge is crucial for understanding how AI operates and what its potential applications are in various academic disciplines.” This is a particularly strong statement in that it moves beyond GenAI to the underlying principles of other AI applications. A second point to touch on here is “Integration into teaching” under the practical application category. The target competency here is presented as “Faculty should be encouraged to integrate AI tools into their daily teaching activities. This includes using AI to personalize learning experiences, manage course content, and assess AI-informed assignments.” There are other valuable suggestions in this document, though it should be emphasized that the target is university-level education, driven by goals of learning subject content rather than by achieving interactional proficiency, as is the case for much of language learning.

ISTE (2024). Widely considered a major leader in technology and education, ISTE has provided technology standards for teachers for decades. TESOL drew on ISTE standards in the development of their framework for English language teachers and learners (TESOL, 2008). We focus here on the ISTE white paper on AI (ISTE 2024). It offers a framework for updating teacher education programs, which they call “Educator Preparation Programs,” or EPPs. The goal of the framework is to provide guidance in rethinking and restructuring teacher preparation. Although not primarily concerned with in-service learning, ISTE’s AI EPP framework is also relevant for teacher educators and institutions that work with in-service teacher professional development.

The framework is built around three major elements “to guide EPPs in their journey to integrate AI competencies into their programs” (p. 1). The first element, Vision, challenges EPPs to evaluate current faculty and teacher candidate attitudes toward AI and tools in use, followed by developing both short and long-term objectives and a process for tracking progress and achievement. The second element, Strategy, includes deepening faculty understanding of GenAI and how it works, modeling GenAI skills for teacher candidates and providing a foundation in GenAI ethics. The third element, Support, covers the importance of each EPP developing a governance structure at the institutional level, “that addresses accountability mechanisms, transparency, application, ethics, and impact assessment, among other aspects” (p. 6). The framework document includes additional details and illustrative examples useful to language programs and teacher educators involved with in-service teachers rather than initial preparation. Other relevant resources from ISTE are available at https://iste.org/ai.

Paradox Learning (Lee, 2023; Lee, 2025). Paradox Learning is a private company providing elearning solutions and training with an emphasis on developing AI literacy for educational clients. They have created a number of AI resources for educators and made them available open access. Pratschke (2024) recommends one of these, AI Toolkit for Educators (Lee, 2023), and we briefly review it here. In the first part, Lee addresses the question of why to use AI in education, noting how it can be used to support students with resources, personalize learning, provide real-time monitoring and intervention, and aid faculty in administration, research, and teaching. The next part looks at AI tools for administrative and workflow automation, research, and teaching and learning. This is followed by a discussion of challenges and limitations of AI in six areas (e.g., unequal access), with a case study to illustrate each. The next and most innovative element of Lee (2023) is a seven-part framework for what she calls AI literacy (primarily GenAI but including other types), covering both teachers and learners: fundamentals of AI, data fluency, critical thinking and fact checking, common AI applications, AI ethics, AI pedagogy, and future of work.

An update of the AI literacy competencies appears in Lee (2025). The framework is similar to the 2023 AI Toolkit but replaces “common AI applications” with “diverse use cases” and adds an eighth category of “assessment”, clearly needed in the education sector. Like the UNESCO framework, it distinguishes three distinct levels of AI literacy competencies, which Lee labels explorer, integrator, and pioneer. For example, in the assessment category at the lowest “explorer” level, Lee proposes the following target competencies: “List commonly used AI tools for assessment. Describe AI assessment tools’ primary functions, such as automated scoring and personalized feedback. Describe the benefits and limitations of AI in assessments, including efficiency, scalability, and inclusivity” (p. 6).

Beyond these four sources for determining AI competencies in education, those interested in a more comprehensive overview of GenAI in education can turn to Pratschke (2024), Generative AI and Education: Digital Pedagogies, Teaching Innovation and Learning Design. An overview of AI issues for language teaching can be found in Edmett et al. (2024), Artificial intelligence and English language teaching: Preparing for the future. We recommend that teacher educators in particular become familiar with the content of these excellent resources.

As is clear from the preceding review, there is a great deal of overlap among AI competency frameworks in terms of the areas they recommend addressing, even though each is targeting a different population. All four point to the importance of knowing the fundamentals of what GenAI is and by extension what it is not. This suggests that teachers who simply jump in and use GenAI without that foundation face unnecessary challenges compared to those who have taken the time to build a basic understanding of GenAI and its range of functionalities for language learning.

As a first step for teachers, we recommend starting with an overview of GenAI to survey the range of issues and options. In addition to the information and insights in the first half of this paper and in the sources noted above, there are many others freely available online in text or video format that provide accessible surveys of GenAI for educational purposes. For example, the Wharton School at the University of Pennsylvania has produced a five-part Introduction to AI for Teachers and Students that is particularly useful because teacher and student perspectives are considered together: https://www.youtube.com/watch?v=t9gmyvf7JYo.

References

Educause. (2024). AI literacy in teaching and learning: A durable framework for higher education. https://www.educause.edu/content/2024/ai-literacy-in-teaching-and-learning/faculty-altl

International Society for Technology in Education (ISTE). (2024). Evolving teacher education in an AI world. https://1818747.fs1.hubspotusercontent-na1.net/hubfs/1818747/2024_ISTE_whitepaper_EvolvingTeacher_Ed_in_an_AI_World.pdf.

Lee, S. (2023). AI toolkit for educators. Paradox Learning. https://paradoxlearning.com/wp-content/uploads/2023/09/AI-Toolkit-for-Educators_v3.pdf

Lee, S. (2025). AI literacy framework for educators & learning professionals. Paradox Learning. https://paradoxlearning.com/wp-content/uploads/2025/03/AI-Literacy-Framework_updated_031325.pdf

UNESCO. (2024). AI competency framework for teachers. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000391104

Education and AI: Tool versus tutor

Of course, a language teacher is more than a benevolent conversation partner. In AI, an intelligent tutoring system (ITS) would be more akin to a language teacher than a chatbot would. An ITS consists of three interacting components (see Heift & Schulze, 2007):

The expert model, which captures the domain knowledge or the information that students should learn;
The tutor model, which makes decisions about the instructional sequences and steps as well as appropriate feedback and guidance for the group as a whole and for individual students;
The student model, which records and structures information about the learning progress and instruction received, domain beliefs and acquired information, as well as the learning preferences and styles of each student.

This is part of a draft of an article I wrote with Phil Hubbard. 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 of Generative AI.
We call this PD sustained integrated PD (GenAI-SIPD). Sustained because it is continuous and respectful of the other responsibilities and commitments teachers have; integrated because the PD activities are an integral part of what teachers do anyway; the teacher retains control of the PD process.

Only if the sole learning objective is conversational ability, can one assume that the LLM has elements of an expert model. The other two models, however, cannot be mimicked by a GenAI tool. Consequently, teachers still have to teach – determine instructional sequences, time appropriate feedback, remember and work with an individual student’s strengths and weaknesses – also when using GenAI tools in various phases of the learning process. GenAI tools can provide multiple ideas for engaging learning activities, texts for reading with a ready-made glossary, or drafts of an entire unit or lesson plan. However, it is the teacher who must understand, select, adapt, and implement them. The entire teaching process and its success are still the responsibility of the teacher.

Grammar teaching in antiquity — Grammar teaching in Ancient Rome (generated by ChatGPT 5.1)

In an educational institution, teachers can meet this responsibility because learners normally trust their expert knowledge, because teachers have been trained, certified, and frequently evaluated. The same is not (yet) true of GenAI tools. They have been trained through machine learning, but their semantic accuracy and pragmatic appropriateness have often been found lacking. The generated text is plausible, but not necessarily factually correct or complete. This way, GenAI output is an insufficient basis for successful learning. This becomes apparent not only when one tries out a GenAI tool in the area of one’s own expertise, but also when one looks back on what teachers have said about the various levels of trustworthiness of internet texts, which also formed the basis for the machine learning for LLMs, for the last thirty years: sources have to be checked and validated. In machine learning for LLMs, the texts and sources are not checked nor validated. This can impact the content accuracy of LLM output. Of course, learners cannot be expected to check the accuracy of information they are only about to learn; believing the truth value of the information is a prerequisite for learning. Critical analysis and questioning the information learnt is always a second step. Also, first studies have emerged that show that GenAI can create the illusion of knowing and thus of learning (Mollick, 2024); consequently, chatbots are not always a tool for successful learning.

The main thing to remember is: these GenAI chatbots are a tool and not a tutor – more like a hammer than an artisan, more like a dictionary than an interpreter, and more like an answering machine (remember those?) than a teacher.

References

Heift, Trude and Mathias Schulze (2007). Errors and intelligence in CALL: Parsers and pedagogues. Routledge.

Mollick, E. (2024). Post-apocalyptic education: What comes after the homework apocalypse. https://www.oneusefulthing.org/p/post-apocalyptic-education