21 mar 25 jumT

 

PAGI KUL ONLINE

jumatan

anisa

toko teh

mlm taraweh

allahuakbar

I hope you show me the way

 

God, forgive me for being greedy,
You’ve given me a scholarship, yet I still want more,
But my heart always yearns for more,
The blessings that come never seem enough, God.

Here, it feels so very comfortable,
Every step is filled with gratitude,
I am so happy here, God,
Living in a peace that cannot be expressed.

I want to live here, always,
Enjoying every moment you give,
Surrounded by those who encourage,
Building a future full of hope.

I hope you show me the way, God,
To reach dreams that are even higher,
With a sincere heart and full effort,
So that every hope becomes a reality.

God, I ask that you don’t let me become complacent,
Let me keep fighting on this path,
With all that you’ve given me,
I will remain thankful and continue striving.

But if they come home late,

 

It should be prayer at the right time,
Honoring time, a gift from God.
Honoring those who arrive early,
With sincerity, no one left behind.

Pity those who arrive early,
With hopes of doing what's best.
They come with pure intentions,
But sometimes time passes too fast.

They want to arrive early,
Pray at the right time, without rushing.
Go home on time, back to rest,
Ready for tasks, nothing left behind.

But if they come home late,
Tired, weary, their body lies still.
Sleep comes quickly, tasks left undone,
Hopes delayed, forgotten.

Yet, even when time flies by,
There’s always a lesson, we must try.
Pray on time, tasks complete,
Maintain balance, for life to be sweet.

What matters is that I am in His circle,

 

Excluded, it's okay, it doesn't matter to me,
No need to join, no need to be seen by anyone.
They may be busy with their own world,
But my heart remains calm, unmoved.

What matters is that I am in His circle,
A place full of love, full of His grace.
In God, I find my place,
More than the world, more than they know.

They seek recognition, a place to belong,
But I find peace that cannot be denied.
His circle is the true home,
There, I feel whole, I feel I matter.

No need to be included in this narrow world,
Because His love is the closest place to me.
God is always present, in every step,
Strengthening my heart, giving hope that never fades.

In His circle, I stand firm,
Unaffected by a world that sometimes seems gray.

I am where I belong.

 

Their uni circle, so tight and true,
With bonds that form, and dreams that grew.
A world of voices, opinions they share,
Yet, somehow, I feel an absence there.

Not include that too, they chose to steer,
A quiet space where none would peer.
But in the silence, a truth I know,
There’s more to life than what they show.

It’s ok, I whisper, as peace descends,
For I have found where love never ends.
In the circle of God, I belong,
A place of grace, where I grow strong.

Their world may be vast, their paths so wide,
But my heart rests in the love inside.
No need for inclusion, or to belong,
For in His arms, I am where I belong.

Their uni circle will fade away,
But God’s circle, forever will stay.

Critical Incident Method (CIM)

 

The Critical Incident Method (CIM) can significantly aid innovation in elementary school education by identifying key moments that impact learning experiences. Here’s how it helps:

1. Encouraging Reflective Practice for Teachers

• Teachers can analyze critical incidents (both positive and negative) in the classroom to assess what works and what needs improvement.
• Reflection leads to innovative teaching strategies and curriculum adjustments that enhance student learning.

2. Enhancing Problem-Solving Skills

• By documenting and discussing critical incidents, educators can develop creative solutions to classroom challenges.
• Encourages a culture of continuous improvement in teaching methodologies.

3. Supporting AI and Technology Integration

• Teachers can track critical incidents involving technology use (e.g., challenges in AI-based learning tools).
• Helps in refining technology integration strategies to make AI tools more effective for elementary education.

4. Personalizing Learning Approaches

• Identifying patterns in student behavior through critical incidents can guide personalized learning.
• Educators can adjust lesson plans to fit individual learning needs.

5. Fostering a Growth Mindset in Students

• Sharing critical incidents with students can help them understand the importance of learning from mistakes.
• Encourages resilience and innovative thinking among young learners.

6. Shaping Policy and Teacher Training

• Data from critical incidents can inform policy changes and professional development programs.
• Helps in designing AI-era competencies needed for future teachers, aligning with your research focus.

Lecturers in the Elementary School Education department typically face several critical events throughout their careers.

 

Lecturers in the Elementary School Education department typically face several critical events throughout their careers. These events can impact their teaching, research, and professional development. Some key challenges include:

1. Curriculum Changes & Policy Shifts

• Frequent updates in national education policies and curriculum revisions may require lecturers to adapt their teaching materials and methods.
• In Indonesia, shifts toward Merdeka Belajar (Independent Learning) impact how lecturers prepare future elementary teachers.

2. Integration of Technology & AI

• The increasing role of AI in education forces lecturers to integrate digital literacy and AI-related competencies into their courses.
• Resistance from some lecturers due to a lack of digital skills or institutional support can be a barrier.

3. Accreditation & Quality Assurance

• Universities must maintain accreditation standards, requiring lecturers to align their teaching, research, and assessments with national or international benchmarks.
• Increased pressure to produce high-quality graduates who meet industry and societal needs.

4. Student Competency Gaps

• Many students entering the education field may lack critical thinking, problem-solving, or digital literacy skills, requiring lecturers to address these gaps.
• Balancing foundational pedagogy with future skills like creativity and adaptability.

5. Research & Publication Pressure

• Universities often demand research productivity, but balancing teaching loads, community service, and administrative tasks can be challenging.
• Access to funding and publication in reputable journals is a common hurdle.

6. Community & Industry Engagement

• Collaborating with elementary schools for practicum programs can be challenging due to bureaucratic barriers.
• The need for lecturers to engage in community service activities as part of their professional responsibilities.

7. Student Practicum & Fieldwork Challenges

• Ensuring students receive quality field teaching experiences, especially in rural or under-resourced schools.
• Managing student expectations and preparedness for real-world classroom challenges.

8. Funding & Resource Constraints

• Limited budgets for educational innovation, research, and faculty development.
• Difficulty accessing grants or financial support for technology-driven teaching methods.

crisis management strategy

 

Universities typically use a crisis management strategy that involves several key response strategies when facing a major crisis. These strategies include:

1. Crisis Communication Strategy

• Transparency & Timely Updates: Provide clear, honest, and regular updates to students, faculty, staff, and stakeholders.
• Centralized Communication Channel: Use official websites, social media, emails, and press releases to maintain control of information.
• Spokesperson & Leadership Visibility: Designate a credible spokesperson (such as the university president or PR officer) to address the crisis publicly.

2. Operational & Logistical Response

• Emergency Preparedness Plan: Activate pre-planned emergency protocols (e.g., evacuation, remote learning, cybersecurity measures).
• Resource Allocation: Mobilize funds, technology, and personnel to address the crisis effectively.
• Collaboration with Authorities: Work closely with government agencies, law enforcement, or health departments depending on the crisis type.

3. Student & Faculty Support

• Mental Health & Counseling Services: Provide psychological support for those affected.
• Academic Flexibility: Adjust academic policies (e.g., online learning, deadline extensions, grading flexibility).
• Financial Assistance: Offer scholarships, emergency grants, or tuition adjustments if needed.

4. Reputation & Public Relations Management

• Crisis Narrative Control: Address misinformation and rumors with factual updates.
• Engaging Stakeholders: Involve alumni, donors, and community partners in crisis recovery.
• Post-Crisis Reflection & Reputation Repair: Conduct reviews, apologize if necessary, and implement reforms to rebuild trust.

5. Post-Crisis Evaluation & Policy Improvement

• Lessons Learned Analysis: Identify strengths and weaknesses in the response.
• Policy Revisions: Strengthen crisis management plans for future incidents.
• Stakeholder Feedback: Gather feedback from students, faculty, and the public to improve future responses.

 

CHAPTER 2: LITERATURE REVIEW

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:

1. SLT in teacher education as a theoretical framework.

2. AI-driven pedagogical transformations in global and Indonesian contexts.

3. 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.

---

2.4 Indonesian Education Policies & AI Integration

2.4.1 Alignment with National Policies

The Indonesian government has launched several initiatives to integrate AI in education:

---

2.5 Conclusion & Research Gap

Existing research has extensively explored AI integration in education but lacks:

• Studies applying SLT to AI-based teacher training.

• Empirical research on AI-powered apprenticeships for pre-service teachers in Indonesia.

This study seeks to fill these gaps by investigating how SLT can inform AI-era teacher competency development in Indonesia.

 

CHAPTER 2: LITERATURE REVIEW

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:

1. SLT in teacher education as a theoretical framework.
2. AI-driven pedagogical transformations in global and Indonesian contexts.
3. 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.

---

2.4 Indonesian Education Policies & AI Integration

2.4.1 Alignment with National Policies

The Indonesian government has launched several initiatives to integrate AI in education:

---

2.4.2 Challenges in AI-Era Teacher Training in Indonesia

Despite these policy initiatives, significant barriers remain:
✔ Lack of AI training for teachers (Indonesia Education Report, 2023).
✔ Infrastructure gaps, especially in rural schools (World Bank, 2022).
✔ Teacher resistance to AI adoption (Mustafa et al., 2023).

These challenges highlight the need for AI-based teacher training programs grounded in Situated Learning Theory.

---

2.5 Conclusion & Research Gap

Existing research has extensively explored AI integration in education but lacks:

• Studies applying SLT to AI-based teacher training.
• Empirical research on AI-powered apprenticeships for pre-service teachers in Indonesia.

This study seeks to fill these gaps by investigating how SLT can inform AI-era teacher competency development in Indonesia.

 

Literature Review: Core Competencies for Future Elementary School Teachers in the AI Era

Guided by Situated Learning Theory (SLT)

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).

---

5. Conclusion & Research Gap

Existing research highlights the importance of AI integration in teacher education but lacks:

• Studies applying SLT to AI-based teacher training.
• Empirical research on AI-powered apprenticeships for pre-service teachers in Indonesia.

Thus, this study seeks to explore how SLT can inform AI-era teacher competency development in Indonesia.

 

Research Framework: AI-Era Core Competencies for Future Elementary School Teachers in Indonesia

Guided by Situated Learning Theory (SLT)

1. Theoretical Foundation

This study is grounded in Situated Learning Theory (SLT) by Lave & Wenger (1991), which emphasizes that:
🔹 Learning occurs best in authentic, social, and practice-based environments.
🔹 Knowledge is context-dependent, gained through participation in real-world tasks.
🔹 Newcomers (student teachers) learn through Legitimate Peripheral Participation (LPP) in Communities of Practice (CoP).

2. AI-Era Core Competencies for Future Teachers

Aligned with Indonesian education policies (Merdeka Belajar, Guru Penggerak, National Digital Literacy Movement), future teachers must develop:

---

3. Research Questions

1️⃣ How can Situated Learning Theory inform AI-based teacher training in Indonesia?
2️⃣ What AI-related competencies are most crucial for future elementary school teachers?
3️⃣ How effective are AI-powered Communities of Practice (CoP) in professional teacher development?
4️⃣ How do pre-service teachers engage with AI-powered cognitive apprenticeships?

---

4. Research Methodology

🔹 Research Design:

• Qualitative (Case Study / Ethnography) → Observing AI-integrated teacher training programs.
• Mixed Methods (if including AI competency assessments).

🔹 Participants:

• Pre-service teachers in Indonesian teacher education programs.
• Mentor teachers & AI-education experts.

🔹 Data Collection Methods:

✔ Interviews & Focus Group Discussions (FGDs) → Understanding teachers’ perceptions of AI integration.
✔ Observations of AI-Augmented Teaching → Studying AI-supported training environments.
✔ Document Analysis → Reviewing AI-driven teacher education policies.

---

5. Contribution & Implications

✔ Provides a contextualized AI-based teacher training model.
✔ Supports Indonesian education policy implementation (Merdeka Belajar, Guru Penggerak).
✔ Guides curriculum development for teacher education programs.

Framework: Situated Learning & AI-Era Teacher Competencies in Indonesia

 

Framework: Situated Learning & AI-Era Teacher Competencies in Indonesia

1. AI-Powered Communities of Practice (CoP) & Merdeka Belajar

🔹 Policy Connection:

• The Merdeka Belajar policy encourages collaborative and flexible learning.
• AI-powered teacher learning communities align with this, enabling continuous professional development (CPD).

🔹 Application:

• Online AI-Powered Teaching Forums (e.g., AI-enhanced Ruang Guru, Google for Education Indonesia).
• Government-supported teacher AI training through Kampus Merdeka.

🔹 Competencies Developed:
✔ Digital collaboration
✔ AI integration in lesson planning
✔ Adaptive teaching strategies

---

2. Legitimate Peripheral Participation (LPP) through AI-Augmented Teaching

🔹 Policy Connection:

• Micro-credentials & internships (Kampus Merdeka Program) emphasize learning-by-doing.
• AI tools can facilitate early exposure to real classrooms.

🔹 Application:

• AI-driven virtual teaching assistant training for pre-service teachers.
• Hybrid internships where trainees work alongside AI-assisted educators.

🔹 Competencies Developed:
✔ AI-enhanced pedagogy
✔ Classroom management with AI analytics
✔ Personalized student learning pathways

---

3. Cognitive Apprenticeship & AI Mentorship (Guru Penggerak Program)

🔹 Policy Connection:

• The Guru Penggerak (Transformative Teacher) Program supports mentorship-based teacher training.
• AI-powered mentoring systems can personalize guidance for future teachers.

🔹 Application:

• AI-powered self-reflection dashboards for teacher trainees.
• Mentorship matching using AI, pairing new teachers with experienced educators.

🔹 Competencies Developed:
✔ AI-based student assessment
✔ Data-driven decision-making
✔ Critical thinking in AI ethics

---

4. AI-Supported Classroom Simulations & National Digital Literacy Movement

🔹 Policy Connection:

• The Digital Literacy Movement promotes responsible technology use in education.
• AI-driven simulations can train teachers on AI ethics and bias in learning.

🔹 Application:

• AI-based classroom simulations for handling student behavior.
• VR + AI environments for realistic teaching practice.

🔹 Competencies Developed:
✔ AI ethics & responsible AI use
✔ Problem-solving in AI-enhanced classrooms
✔ Real-time adaptive teaching

---

Recommendations for Teacher Education Programs in Indonesia

🔹 Integrate AI-Powered Learning Communities in pre-service training.
🔹 Develop AI-driven mentorship platforms for novice teachers.
🔹 Adopt AI-enhanced apprenticeships in teacher internships.
🔹 Use AI classroom simulations in teacher education curricula.

Applying Situated Learning Theory to AI-Era Teacher Competencies

 

Applying Situated Learning Theory to AI-Era Teacher Competencies

Since your research focuses on the core competencies needed in an AI era for future elementary school teachers in Indonesia, Situated Learning Theory (SLT) can provide a framework for teacher education programs by emphasizing authentic, social, and contextualized learning experiences. Below is how SLT aligns with AI-era teacher competencies:

---

1. AI-Powered Communities of Practice (CoP)

🔹 How it Works:

• Teachers collaborate in online and offline communities to share AI-integrated teaching strategies.
• Platforms like AI-enhanced professional learning networks (PLNs) allow teachers to discuss best practices and challenges.

🔹 AI-Competency Connection:
✔ Digital literacy → Using AI-powered teaching tools
✔ Collaboration & adaptability → Engaging with AI-driven communities

🔹 Example:

• A group of Indonesian teachers uses an AI-powered lesson planning tool and shares insights on what works best.

---

2. Legitimate Peripheral Participation (LPP) through AI-Augmented Teaching

🔹 How it Works:

• Future teachers start as observers, interacting with AI tools in actual classrooms.
• Gradually, they take on more responsibility (e.g., co-teaching with AI assistance).

🔹 AI-Competency Connection:
✔ AI pedagogy → Integrating AI in lesson delivery
✔ Ethical AI use → Understanding AI biases and limitations

🔹 Example:

• A student teacher first observes how an AI-based tutoring system adapts to different learning styles.
• Later, they co-teach using AI-generated lesson modifications.

---

3. Cognitive Apprenticeship with AI Mentors

🔹 How it Works:

• New teachers learn by doing under the guidance of experienced educators and AI-powered teaching assistants.
• AI-driven feedback systems provide personalized coaching for lesson improvement.

🔹 AI-Competency Connection:
✔ Adaptive teaching → Using AI insights for differentiated instruction
✔ Data literacy → Interpreting AI-generated student performance reports

🔹 Example:

• A novice teacher receives feedback from an AI-driven classroom analytics tool, learning which students need more help.

---

4. Contextualized Learning via AI-Supported Simulations

🔹 How it Works:

• Future teachers engage in AI-driven classroom simulations before teaching real students.
• Virtual Reality (VR) + AI allows realistic classroom problem-solving scenarios.

🔹 AI-Competency Connection:
✔ Critical thinking → Making AI-informed teaching decisions
✔ Problem-solving → Managing diverse classroom challenges with AI insights

🔹 Example:

• A teacher-in-training practices handling student disruptions in an AI-powered virtual classroom.

---

Integrating SLT & AI in Indonesia’s Teacher Education

To prepare teachers for an AI-driven classroom, training programs should:
✅ Build AI-supported Communities of Practice for ongoing learning.
✅ Encourage learning-by-doing through AI-enhanced apprenticeships.
✅ Use AI analytics for self-reflection and feedback.
✅ Develop real-world AI teaching simulations for skill-building.

Situated Learning Theory,

 

Situated Learning Theory, developed by Jean Lave and Étienne Wenger in the early 1990s, emphasizes that learning is inherently social and occurs best in authentic contexts where learners engage in real-world activities. The theory suggests that knowledge is situated—meaning it is deeply tied to the context, culture, and social interactions in which it is learned.

Key Concepts of Situated Learning Theory

1. Learning as Participation

   • Learning is not just acquiring information but becoming a participant in a community of practice.
   • Newcomers (learners) start at the periphery and gradually move toward full participation through engagement in meaningful activities.

2. Legitimate Peripheral Participation (LPP)

   • Beginners learn by participating in real tasks alongside more experienced members.
   • Over time, they take on more responsibility and move toward expertise.

3. Communities of Practice (CoP)

   • Groups of people who share a passion or interest in something and learn through collaboration.
   • Examples: Teachers sharing best practices, programmers in open-source communities, or apprentices learning from skilled workers.

4. Contextualized Learning

   • Knowledge is best acquired and applied in the same context where it will be used.
   • Real-world settings, apprenticeships, and problem-based learning align with this principle.

5. Cognitive Apprenticeship

   • Mentors guide learners by modeling skills, coaching, and allowing them to practice in authentic settings.
   • Example: A student-teacher working under an experienced educator.

Applications in Education

• Project-Based Learning (PBL) – Engaging students in solving real-world problems.
• Internships & Apprenticeships – Learning by working alongside professionals.
• Collaborative Learning – Encouraging group work and peer interactions.
• Role-Playing & Simulations – Providing authentic experiences within a controlled environment.

Relevance to Your Research

Since your research focuses on core competencies needed in an AI era for future elementary school teachers in Indonesia, Situated Learning Theory could be useful in shaping teacher training programs. Future teachers may benefit from:

• Real-world AI-integrated teaching experiences through classroom simulations.
• Communities of Practice where they collaborate with experienced educators using AI tools.
• Mentorship and cognitive apprenticeship to bridge the gap between theory and practice.