1. Introduction

As AI continues to reshape education, teachers must develop new core competencies to effectively integrate AI into their pedagogy. Situated Learning Theory (SLT) (Lave & Wenger, 1991) provides a framework for AI-era teacher training by emphasizing learning in authentic contexts, social participation, and apprenticeship models. This section reviews key literature on:

SLT in teacher education,
AI-driven pedagogical transformations, and
Indonesian education policies supporting AI integration.

2. Situated Learning Theory (SLT) in Teacher Education

2.1 Learning as Participation in Communities of Practice (CoP)

SLT posits that learning is a social process, occurring through participation in Communities of Practice (CoP) (Lave & Wenger, 1991). In teacher education, pre-service teachers learn best through immersion in real classroom settings (Putnam & Borko, 2000).

🔹 Application in AI-era teacher training:

AI-powered online CoPs (e.g., Google for Education, Indonesian teacher networks).
Collaborative AI lesson planning within teacher education programs.

Indonesian Context: The Merdeka Belajar policy emphasizes flexible, collaborative teacher learning, making AI-powered CoPs a key strategy (Ministry of Education and Culture, 2020).

2.2 Legitimate Peripheral Participation (LPP) in AI-Augmented Teaching

SLT highlights Legitimate Peripheral Participation (LPP), where novices start as observers and gradually take on real tasks (Lave & Wenger, 1991).

🔹 Application in AI-powered classrooms:

Pre-service teachers co-teaching with AI tools (e.g., adaptive learning systems).
AI-enhanced teaching internships, where trainees receive AI-generated feedback.

Relevant Study:

Kim et al. (2022) found that AI-assisted teacher apprenticeships improve confidence and adaptive teaching skills in pre-service teachers.

Indonesian Context: The Kampus Merdeka Program encourages experiential learning, which can be enhanced through AI-driven classroom simulations and internships.

3. AI-Driven Pedagogical Transformations

3.1 AI as a Teaching Assistant

AI is increasingly used to support personalized learning (Luckin et al., 2018). AI tools provide:

Automated grading & feedback (Chen et al., 2021).
Personalized student learning analytics (Zawacki-Richter et al., 2019).

Challenges:

Teacher training gaps in AI literacy (Huang et al., 2023).
Ethical concerns (bias, data privacy, over-reliance on AI) (Selwyn, 2022).

Indonesian Context: The National Digital Literacy Movement aims to equip teachers with AI literacy skills, but implementation gaps remain (Indonesia Ministry of Communication and Informatics, 2021).

3.2 AI-Driven Cognitive Apprenticeships

SLT emphasizes cognitive apprenticeship, where learning occurs through scaffolding, modeling, and coaching (Collins et al., 1989).

🔹 Application in AI-era teacher education:

AI-powered mentorship programs matching novice teachers with experienced educators.
AI-based classroom analytics guiding teacher decision-making.

Relevant Study:

Holmes et al. (2023) found that AI mentoring systems enhance reflective teaching practices in novice educators.

Indonesian Context: The Guru Penggerak Program supports mentorship-based training, which can be strengthened through AI-powered mentorship platforms.

4. Indonesian Education Policies & AI Integration

4.1 Merdeka Belajar & AI Pedagogy

The Merdeka Belajar framework promotes student-centered learning and teacher autonomy (Ministry of Education, 2020).

🔹 Potential AI Applications:

Adaptive learning platforms aligning with Merdeka Belajar’s flexible curriculum.
AI-powered teacher professional development.

4.2 Challenges in AI-Era Teacher Training in Indonesia

Despite policy support, challenges include:
✔ Lack of AI training for teachers (Indonesia Education Report, 2023).
✔ Infrastructure gaps in rural schools (World Bank, 2022).
✔ Teacher resistance to AI adoption (Mustafa et al., 2023).