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UDK 004.852
AI-ENHANCED METHODOLOGICAL TRAINING FOR FUTURE TEACHERS
Najmiddinova Gulnora Najmiddin kizi,
PhD Student of Navoi State University
Email:
najmiddinovagulnora01@gmail.com
ORCID ID:
Abstract:
Artificial intelligence (AI) is transforming education, but its integration into teacher
preparation remains underdeveloped. This article reviews current approaches to AI in teacher
training and proposes a structured framework for embedding AI technologies into pre-service
and in-service teacher education. We outline a staged methodology – from foundational AI
literacy to immersive practice and reflective assessment – aligned with emerging competency
frameworks. The model leverages adaptive learning systems, AI tutors, chatbots, and simulations
to personalize and enrich methodological training. Benefits include individualized instruction
and increased efficiency, while challenges involve equity, privacy, and ethical use of AI. We
discuss implications for pedagogy (e.g. data-driven teaching), curriculum design (incorporating
AI literacy and AI-enabled tools), and teacher competencies (drawing on UNESCO’s five AI
dimensions). Finally, we highlight the need for ongoing research and policy support to realize an
AI-augmented future for teacher preparation.
Keywords:
Equity of access, Bias and Ethics, Teacher attitudes, technical limitations,
Curriculum integration
,
Limitations and Challenges, AI pedagogy.
Introduction
Rapid advances in artificial intelligence (AI) offer unprecedented opportunities in education. AI-
driven platforms can analyze learning data, adapt content, and provide real-time feedback,
fundamentally changing how instruction and training occur. To leverage these tools, future
teachers must be prepared to use and understand AI in teaching. Yet research indicates a gap:
only about 35% of studies on AI in education address teacher professional development, with
most focusing on student learning. Likewise, UNESCO notes few countries have defined AI
competencies for educators. This growing consensus suggests a need for systematic AI
integration into teacher education. In response, we review existing approaches – from adaptive
learning systems to AI tutors – and propose a comprehensive methodological framework for AI-
enhanced teacher training. We then examine the benefits, limitations, and challenges of AI-based
teacher education, and discuss implications for pedagogy, curriculum design, and teacher
competency frameworks.
LITERATURE REVIEW.
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Recent practice and research reveal several ways AI is already entering teacher education.
On
the technology side, adaptive learning systems (e.g. Knewton, Squirrel AI) and AI tutors (e.g.
Carnegie Learning, DreamBox) provide models of personalized instruction that teachers can
study and emulate. Chatbots and language models like ChatGPT are used informally by
educators for tasks such as lesson planning or answering content questions. Some teacher-
training programs have begun introducing AI literacy courses or modules, following
international guidelines (for example, the UNESCO AI competency framework). Innovative field
experiences use AI-based simulations: e.g. mixed-reality classroom simulators (such as
TeachLivE) let trainee teachers practice managing virtual students as in a “flight simulator” for
teaching .
On the professional development side, models are emerging that blend AI tools with teacher
learning. Tammets and Ley (2023) propose teacher-centered design: teachers co-design and
interact with AI tools that support classroom decision-making, noticing student needs and
adapting instruction.
In-service programs increasingly use AI analytics for reflection – for
instance, recording a lesson and using AI to analyze teaching moves. However, reports indicate
that teacher preparation programs often treat AI as an add-on rather than integrated content, and
few curricula systematically address AI’s pedagogical use. Notably, global surveys (e.g.
UNESCO 2024) call for restructuring teacher training around AI competency, emphasizing
ethical, human-centered use.
Overall, current approaches are piecemeal. Many educators recognize AI’s future importance – a
Walton Family Foundation survey found 71% of U.S. teachers agree AI tools will be essential
for student success – but training lags behind. To address this, we outline below a structured
methodology that systematically embeds AI into teacher education.
METHODOLOGY.
We propose a multi-phase framework for “AI-augmented teacher education” that aligns with
emerging competency standards. This approach ensures teachers
acquire
AI knowledge,
deepen
practical skills, and eventually
create
AI-informed teaching innovations. The framework has
four main stages:
1.Foundational AI Literacy (Acquire):
Pre-service teachers begin with the basics of AI – what
AI is, how it works, and its ethical implications. Coursework covers AI concepts (e.g. machine
learning, chatbots), data literacy, and AI ethics, reflecting UNESCO’s dimensions of
human-
centered mindset
and
ethics of AI
. Role-playing and case studies can illustrate real-world
scenarios (e.g. bias in algorithms, privacy issues) to develop critical perspectives.
2.Integration of AI Tools (Deepen):
Trainees learn to use specific AI-enabled educational tools
as part of their methodological training. For example, they might interact with adaptive learning
systems or AI tutoring platforms to see how content can be tailored to individual students.
Chatbots (e.g. GPT-driven conversational agents) can serve as practice partners – for instance,
posing as a student asking subject questions, while the teacher practices responding. During
pedagogy courses, AI tools are incorporated in lesson design exercises: teachers might use
generative AI to draft lesson plans or get feedback on activity scripts. Throughout this phase,
instructors model a
human-AI collaboration
approach, where the teacher guides and interprets AI
outputs.
1
Najmiddinova G.N. “The Integration of Artificial Intelligence (AI) into education system”.34-37 b. Tamaddun
Nuri ,ISSN 2181-8258,12-son (63) Ilmiy, ijtimoiy-falsafiy, madaniy-ma’rifiy, adabiy-badiiy jurnal,12.12.2024.
https://doi.org/10.69691/r1bx4f56
2
Tammets, K., & Ley, T. (2023). Integrating AI tools in teacher professional learning: A conceptual model and
illustrative case. Frontiers in Artificial Intelligence, 6, 1255089. https://doi.org/10.3389/frai.2023.1255089
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3.Immersive Practice and Simulation (Deepen/Create):
Building on AI tool experience,
teachers engage in interactive simulations. Immersive environments (such as virtual/augmented
reality classrooms) provide risk-free settings. For instance, using a virtual classroom simulator
(like TeachLivE ), a trainee can practice classroom management and receive AI-driven
feedback on their prompts and responses. This stage emphasizes reflective practice: AI analytics
from the simulation (e.g. a log of teacher questions, student engagement metrics) are used to help
teachers refine strategies. Peer collaboration can be enhanced by AI co-facilitators or automated
discussion moderators in group projects.
4.Reflective Evaluation and Continuous PD (Create):
Finally, teachers use AI-driven
analytics to assess their own learning and plan further growth. E-portfolios and video logs of
teaching can be analyzed by AI for insights (e.g. frequency of student talk, use of inclusive
language). This aligns with UNESCO’s “AI for professional development” dimension. Teachers
also learn to critically evaluate AI systems themselves – seeking “explainable, overridable” AI as
recommended by education authorities. The framework encourages a feedback loop: trainers
collect data on trainee performance and iterate the curriculum using design-based research
methods, ensuring tools and content adapt to teacher needs.
This structured progression (Acquire–Deepen–Create) mirrors international AI competency
frameworks and allows for assessment at each level (e.g. through AI-themed capstone projects or
practicum evaluations). In sum, the methodology integrates AI not as a side topic but as a core
element of methodological training: teachers learn
with
AI and
about
AI in parallel.
RESULTS AND DISCUSSION.
Benefits:
AI-enhanced teacher training offers notable advantages. First,
personalization
: AI-
driven platforms can adapt training content to each trainee’s knowledge and pace, addressing
individual strengths and gaps. Just as AI tutors tailor lessons to students, an AI-powered learning
environment could provide extra practice on topics where a trainee is weak. Second,
efficiency
:
repetitive tasks (e.g. auto-grading microteaching exercises, generating quizzes) can be offloaded
to AI, freeing instructors to focus on higher-level mentoring. Pardos (EdWeek) notes AI can
“save time and money… not by doing away with teaching roles but by cutting down on…
instructional materials”. Third,
enhanced feedback loops
: AI provides immediate formative
feedback (e.g. on lesson plan drafts or practice teaching), allowing trainees to iterate quickly. For
example, an AI tutor might score a mock lesson and highlight areas for improvement, much
faster than a human observer. Fourth,
safe practice environments
: simulated classroom AI
(avatars, VR) lets teachers rehearse challenging scenarios (e.g. handling disruptions) without risk
to real students . Studies have shown that such simulations can improve teachers’ skills and
confidence. Collectively, these benefits point to more engaged, confident trainees who receive
data-informed support throughout their preparation.
Limitations and Challenges:
Despite promise, AI-based training faces obstacles.
Equity of
access
is a major concern: high-end AI tools and infrastructure may be available only in well-
funded programs. WEF warns that without deliberate policy, AI resources could widen gaps
between schools, especially in under-resourced regions.
Bias and Ethics
are critical issues: AI
systems trained on limited data may give skewed feedback, and their use in teaching must
respect student privacy and fairness. The U.S. Department of Education cautions about
“examples of discrimination from algorithmic bias,” underscoring the need for oversight. This
means teacher training must include safeguarding against biased AI and respecting data rights.
Teacher attitudes
are another challenge: some educators fear AI may replace their role. In
practice, most experts reject this fear, emphasizing augmentation over replacement, but
overcoming skepticism requires careful PD and evidence of AI’s assistive role.
Curriculum
integration
is nontrivial: deciding where and how to embed AI in already-crowded teacher
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education programs demands strategic planning. As one AI researcher notes, heavy reliance on
vendor platforms could risk ceding curriculum control to tech companies . Finally, there are
technical limitations
: not all teacher skills are easily measured or supported by AI (e.g. empathy,
creativity), and current chatbots may generate inaccurate or superficial suggestions.
In summary, AI-based teacher training can greatly enrich learning, but must be implemented
with attention to digital divides, ethical safeguards, and alignment with educators’ goals.
Implications for Pedagogy, Curriculum, and Competencies
Integrating AI into teacher preparation will have ripple effects on pedagogy and program design.
Pedagogically, educators must model a
human-AI collaboration
mindset: teachers’ roles shift
toward mentoring, interpreting AI feedback, and focusing on social-emotional support while
routine tasks are automated. Classrooms are likely to become more data-driven; teachers learn to
use learning analytics to inform instruction (for example, spotting trends in student engagement)
which requires new analytic and reflective skills.
Therefore, teacher educators should emphasize
21st-century pedagogies (inquiry, project-based learning) that complement AI tools, rather than
traditional lecture-only methods.
Curriculum design in teacher education should embed AI literacy throughout. Instead of a single
workshop, AI concepts can be threaded into methods courses, technology courses, and clinical
experiences. For example, a mathematics methods class might include sessions on AI-driven
math tutors and have trainees compare AI-generated problems with their own. Institutes might
partner with edtech providers to give students access to adaptive learning platforms. Assessment
of trainees should likewise evolve: in addition to observation by human supervisors, AI analytics
(such as evaluation of lesson plans by an AI rubric) could become part of proficiency checks.
Key teacher competencies must expand. UNESCO’s framework identifies five AI-related areas.
We can summarize these as follows:
Human-centered mindset:
Teachers should value human agency and be prepared to
make final decisions in teaching, with AI as a support. They must advocate for equity and
inclusivity when using AI.
Ethics of AI:
Educators need competence in ethical principles, such as understanding
privacy, consent, and avoiding bias. Teacher training must build this ethical orientation.
AI foundations and applications:
Teachers require basic AI knowledge (how
algorithms work) and awareness of current tools. This technical foundation allows them to
critically evaluate new technologies.
AI pedagogy:
This is the skill of using AI as a teaching resource. It includes designing
lessons that effectively integrate AI-driven activities (e.g. personalized learning modules) and
interpreting AI-provided insights about student learning.
AI for professional learning:
Finally, teachers need to leverage AI for their own growth
– for instance, using AI-curated resources for self-study or analyzing peer teaching videos with
AI tools.
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Najmiddinova M.N., Najmiddinova G.N. “Pedagogical mechanisms for improving student knowledge with the
help of Artificial Intelligence”. International journal of scientific researchers”. www.wordlyknowledge.uz. Volume:
2, Issue: 1, 2023.
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UNESCO. (2024). AI competency framework for teachers: Guiding teachers on artificial intelligence use and
misuse in education (F. Miao & M. Cukurova, Eds.). United Nations Educational, Scientific and Cultural
Organization.
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Table 1 (below) aligns these competencies with training activities in our methodology. This
competence-based approach ensures that AI training supports teachers’ professional growth, not
just tool usage.
Table 1: AI-Related Teacher Competencies and Training Activities
(Example Framework)
Human-Centered Mindset & Ethics: Classroom debates on AI equity; reflective journals
on AI scenarios.
AI Foundations & Applications: Intro AI modules; hands-on with a coding-free AI tool.
AI Pedagogy: Lesson plan assignments using AI tutors; observation of AI-enhanced
teaching.
AI for PD: Using AI-driven feedback on teaching demonstrations; AI-curated
professional resources.
In summary, teacher education programs must be reoriented around these competencies. In doing
so, curriculum developers and policymakers should engage educators in co-design (as
recommended by Tammets & Ley) to ensure that AI integration supports real teaching needs.
Conclusion
AI holds great promise for transforming teacher preparation, but realizing this requires a
comprehensive, thoughtful approach. We have outlined a structured methodology that starts with
foundational AI understanding, integrates cutting-edge tools into practice, and uses analytics for
continuous improvement. This approach is grounded in emerging frameworks and empirical
insights, and it emphasizes teacher agency and ethics. Benefits of AI-based training include
personalized learning for trainees and greater efficiency, while challenges like equity, bias, and
curricular alignment must be proactively managed.
The implications are broad: teacher educators and institutions will need to update curricula,
invest in technology infrastructure, and develop new assessment strategies. Policymakers should
support these shifts through funding and standards (for example, by promoting UNESCO’s AI
competency framework as a guideline). Future research should evaluate pilot implementations of
this methodology, measuring outcomes such as teacher competence and student learning gains.
By embracing AI as a partner in teacher training, the educational community can prepare a
workforce ready to harness AI for better learning outcomes while upholding human values.
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