2.1 Introduction

The rapid advancement of Artificial Intelligence (AI) is reshaping education, necessitating new core competencies for future elementary school teachers. AI-driven pedagogies require educators to adapt to intelligent learning systems, analyze AI-generated insights, and integrate AI tools for personalized instruction (Luckin, 2018; Zawacki-Richter et al., 2019). However, effective AI-era teacher preparation must be grounded in authentic, practice-based learning environments. This study applies Situated Learning Theory (SLT) by Lave and Wenger (1991) to investigate AI competency development among pre-service teachers in Indonesia.

This chapter reviews existing literature on:

SLT in teacher education as a theoretical framework.
AI-driven pedagogical transformations in global and Indonesian contexts.
Challenges and opportunities for AI adoption in Indonesia’s teacher education system.

2.2 Situated Learning Theory (SLT) in Teacher Education

2.2.1 Learning as Participation in Communities of Practice (CoP)

SLT emphasizes that learning is not an isolated cognitive process but a socially situated practice within Communities of Practice (CoP) (Lave & Wenger, 1991). In teacher education, this means that pre-service teachers develop expertise by participating in professional networks, engaging in collaborative learning, and interacting with experienced educators (Putnam & Borko, 2000).

🔹 AI-Era Application:

AI-powered virtual CoPs enable teachers to exchange knowledge on integrating AI in pedagogy (e.g., Google for Education, Ruang Guru).
AI-driven lesson-planning communities allow teachers to collaboratively refine instructional materials.

🔹 Indonesian Context:

The Merdeka Belajar (Freedom to Learn) framework promotes flexible and collaborative learning (Ministry of Education and Culture, 2020).
AI-powered CoPs align with Indonesia’s teacher development policies, particularly in digital literacy and AI integration.

Empirical Evidence:

Research by Kim et al. (2022) found that AI-supported CoPs enhance teacher digital competencies.
In an Indonesian study, Hidayat et al. (2023) identified that collaborative AI training programs improve AI adoption in elementary education.

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

SLT also introduces the concept of Legitimate Peripheral Participation (LPP), where newcomers learn by gradually participating in real-world tasks (Lave & Wenger, 1991).

🔹 AI-Era Application:

Pre-service teachers co-teaching with AI tools, such as adaptive learning platforms.
AI-enhanced teaching internships where AI provides real-time feedback on lesson effectiveness.

🔹 Indonesian Context:

The Kampus Merdeka Program encourages experiential learning, which can be enhanced through AI-driven classroom simulations.

Empirical Evidence:

Chen et al. (2021) reported that AI-enhanced teaching apprenticeships significantly improve classroom adaptability in new teachers.
Mustafa et al. (2023) highlighted that Indonesian pre-service teachers struggle with AI implementation due to insufficient hands-on training.

2.3 AI-Driven Pedagogical Transformations

2.3.1 AI as a Teaching Assistant

AI is increasingly being integrated into classrooms to support personalized learning, automate assessment, and provide real-time analytics on student progress (Luckin et al., 2018).

🔹 AI-Era Application:

Automated grading and personalized learning analytics help teachers track student performance (Zawacki-Richter et al., 2019).
AI chatbots and virtual assistants support students outside classroom hours (Holmes et al., 2023).

🔹 Indonesian Context:

The National Digital Literacy Movement (2021) promotes AI integration in education, but infrastructure gaps hinder its effectiveness.

Challenges Identified:
✔ Teacher training gaps in AI literacy (Huang et al., 2023).
✔ Ethical concerns (bias, data privacy, over-reliance on AI) (Selwyn, 2022).
✔ Lack of AI-powered classroom tools in rural schools (World Bank, 2022).

2.3.2 AI-Driven Cognitive Apprenticeships

Cognitive apprenticeship, a key element of SLT, emphasizes scaffolding, modeling, and coaching in teacher training (Collins et al., 1989).

🔹 AI-Era Application:

AI-powered mentorship programs connect new teachers with experienced educators.
AI-based classroom analytics provide insights for teacher decision-making.

🔹 Indonesian Context:

The Guru Penggerak Program (Transformative Teacher Program) promotes mentorship-based training, which can be enhanced with AI-powered mentorship platforms.

Empirical Evidence:

Holmes et al. (2023) found that AI mentoring systems enhance reflective teaching practices in novice educators.
An Indonesian pilot study (Putra et al., 2022) demonstrated that AI-driven coaching improves pedagogical decision-making among teacher trainees.