Comprehensive Notes – Module 1: Coding & Artificial Intelligence (KKA) Teacher Training

Introduction / General Description of Module

• Official name: Module 1 “Coding & Artificial Intelligence (AI) Subject in the National Curriculum” for Senior High School (SMA) & Vocational School (SMK) teacher training (Bimtek).
• Released by the Directorate General of Teachers, Education Personnel & Teacher Education, Ministry of Basic & Secondary Education (Kemdikdasmen), 2025.
• Licensing: Creative Commons CC BY-NC-SA 4.0.
• Purpose: Build teacher competence to teach Coding & AI (KKA) contextually, attractively, project-based, and ethically, in line with 21st-century skills.
• Alignment: Based on Academic Paper for Coding & AI; supports RPJPN 2024, Industry 4.0 & Society 5.0, and national digital-transformation priorities.

Training Outcomes, Goals & Indicators

• Training outcome (Capaian Pelatihan)
  • Participants can define core concepts of coding & AI.
  • Apply coding & AI in learning processes.
  • Design lessons that help attain graduate-profile dimensions.
  • Foster ethical values in coding & AI.
• Detailed goals (Tujuan Pelatihan)
  1. Master definitions of coding & AI.
  2. Integrate coding + AI concepts into classroom practice.
  3. Map learning objectives to graduate-profile dimensions.
  4. Cultivate ethics in coding & AI usage.
• Achievement indicators
  • Explain scope & impact of KKA.
  • Explain principles of computational thinking (CT), digital literacy (DL) & AI.
  • Design CT, DL & AI implementation in school.
  • Select appropriate graduate-profile dimensions, elements & sub-elements for each learning objective.
  • Reflect on opportunities & challenges of KKA implementation.

Core Topics (Pokok Bahasan)

1. Introduction to the Coding & AI Subject
   • Background, rationale, goals, characteristics, elements.
2. Scientific Concepts of Coding & AI
   • Computational Thinking, Digital Literacy, AI Literacy & Ethics, AI Utilisation & Development.
3. Implementation of KKA Learning in Schools.

Training Flow (SOLO Taxonomy-based)

Rationale for Introducing Coding & AI in Curriculum

• Human-resource development: Build critical, creative, problem-solving, digital skills for global competitiveness.
• Economic sustainability: Generate new digital-economy opportunities; stimulate tech innovation.
• Innovation & technology for development: Produce young innovators to address social issues.
• Equity in quality education: Ensure all learners, incl. special-needs & low-SES, access digital-skills learning.
• National identity: Use technology to promote local culture globally.

Subject Characteristics

1. Ethics as foundation across all grades.
2. Contextual learning tied to daily or community problems.
3. Flexible delivery: internet-based, plugged, unplugged.
4. Human-centred approach—technology serves people.
5. Scaffolded progression: pre-basic skills in primary, deep CT & DL in lower-secondary, advanced CT & DL in upper-secondary.

Elements & Descriptions

• Computational Thinking (CT): Tiered problem-solving via modelling/simulation; logical, critical, creative reasoning; solo or collaborative.
• Digital Literacy (DL): Skill in creating & disseminating digital content with ethics & safety.
• AI Literacy & Ethics: Concepts, workings, benefits, impacts, critical stance & ethical use of AI.
• AI Utilisation & Development: Applying AI to solve problems, enhance efficiency, and build/improve AI systems.
• Algorithms & Programming (AP): Deriving structured algorithmic texts then coding them with progressively sophisticated paradigms.
• Data Analysis: Structuring, inputting, processing (analyse, infer, decide, predict) & presenting data.

Mapping to Graduate-Profile Dimensions

• Encourages critical thinking, autonomy, digital collaboration, ethical digital citizenship, health & life balance.
• Supports both national citizenship (ethical tech users/creators) & global citizenship (faithful, responsible tech innovators).

Relationship with Informatics Subject

• Informatics is compulsory in Grades 7-10 (elements: CT & DL) and elective in Grades 11-12 (elements: CT, DL, Data Analysis, AP).
• KKA is elective from Grade 5-12, offered where infrastructure & staff allow.
• Teachers must avoid duplication: coordinate topics across CT, DL, AP & Data when both subjects run in parallel.
• KKA emphasises practical, device-rich exploration (plugged & internet-based) beyond merely unplugged activities.

Scientific Concepts of Coding & Programming

• Coding: Translating human intent into computer-understandable instructions using a programming language. Sub-activity of programming.
• Programming: Full software-development lifecycle: planning → analysis → design → implementation (coding) → testing → maintenance.
• Coding serves as gateway to programming concepts and computational logic.
• Delivery modes:
  • Plugged coding (e.g., Visual Studio Code for Python; see screenshot).
  • Unplugged coding (games, simulations, physical activities; e.g., board game for modulus clock).
  • Internet-based interactive platforms.

Artificial Intelligence (AI) Fundamentals

• Multiple definition lenses:
  • Thinking like humans (Haugeland 1985).
  • Acting like humans (Kurzweil 1990).
  • Thinking rationally (Charniak & McDermott 1985).
  • Acting rationally (Poole 1998; Nilsson 1998).
• Kaplan & Haenlein 2019: Ability of a system to interpret external data, learn & apply learning toward goals.
• History highlights:
  • 1950 Turing Test $(\text{can human discriminator identify machine?})$.
  • 1956 Dartmouth conference: birth of term “AI”.
  • 1950s-70s "AI spring", 1980s "AI winter", 2000s resurgence due to big data & compute.
• AI categories:
  • Narrow AI: specific tasks.
  • General AI (AGI): broad human-level cognition (future goal).
• Common approaches: Machine Learning (ML), Deep Learning (DL).

AI Literacy & Ethical Framework (UNESCO 2024)

Computational Thinking (CT) in Depth

• Four pillars (Wing 2006):
  1. Decomposition
  2. Pattern recognition
  3. Abstraction
  4. Algorithm design
• Instructional design steps:
  1. Select problem topic.
  2. Choose/manufacture learning aids (cards, sticky-notes, Scratch, etc.).
  3. Convert aids into active-student learning scenario; embed in lesson docs (modules, worksheets, media, assessments).
  4. Conduct lesson; observe, support, conclude.
  5. Document & evaluate for improvement.
• Example (Modulo clock):
  • Physical 10-hour clock models $(8 + 3) \bmod 10 = 1$; subtraction as backward moves; multiplication as repeated addition.
• Resource hubs: CS Unplugged, Code.org, Bebras Indonesia.

Digital Literacy (DL) Essentials

• Competence to access, organise, evaluate, create & communicate information safely with digital tech.
• Includes: hardware & network basics, algorithms, data analytics, social-media impact, privacy, misinformation discernment, environmental impacts (e-waste, energy).
• UNESCO definition (2018) emphasises safe/appropriate use for work & entrepreneurship.
• Suggested resources: UNESCO media-literacy curriculum, MAFINDO modules, CommonSense Digital Citizenship, Siberkreasi “4 Pillars”, Kominfo “Komdigi”.

AI Literacy & Ethics (K-12)

• Landscape: ML ⊃ DL ⊃ Generative AI.
• Primary-level focus: Demystify AI through everyday examples (voice assistants, image recognition, recommender systems).
• Secondary-level focus: Deeper into supervised/unsupervised/reinforcement learning; model types; societal impacts.
• Key ethical themes:
  1. Bias & fairness (e.g., demographic bias in generative models; see Bianchi 2023 image).
  2. Privacy & data security.
  3. Responsibility & accountability for AI outcomes.
  4. Transparency & explainability.
  5. Copyright in generative outputs.
• Classroom discussion prompts: How might AI decisions affect jobs? Who is liable for an autonomous-vehicle crash? What biases appear in facial recognition across demographics?

AI Utilisation & Development Examples

• Elementary: Image-recognition apps to classify animals; gamified adaptive learning; caution against over-dependence.
• Middle/High:
  • Use generative AI as virtual tutor, language coach, grammar assistant.
  • Creative projects: essays with AI-generated outline, digital art, AI-composed music.
  • Data analysis projects using public datasets & AutoML tools.
  • Drone + AI for precision agriculture: crop monitoring, disease detection (see figure).
• Technologies to introduce:
  1. Speech recognition (Google Assistant, Siri, Alexa; real-time translation; transcription apps like Otter/Fathom).
  2. Image recognition (security CCTV, autonomous vehicles, medical imaging for tumour detection).
  3. Recommendation & prediction engines (YouTube, Netflix, e-commerce).
  4. Generative AI (LLM, GAN): text, code, images—and associated ethical guidelines.
• Pedagogies: Project-based learning (PBL), game-based, inquiry, collaborative design.

Algorithms & Programming (AP)

• Definitions (KBBI): Algorithm = systematic steps; Program = sequence of commands executed by computer.
• From algorithm to code (coding) → compiled/interpreted into runnable program.
• Algorithm representation methods: prose, flowchart, pseudocode.
• Programming language tiers
  • Machine language (binary) – hardware specific.
  • Assembly – symbolic, still hardware specific.
  • High-level language – human-readable, portable (Python, C, Java…). Only high-level expected in K-12.
• Language processors
  1. Compiler – translates to object code; online compilers recommended.
  2. Interpreter – executes line-by-line (e.g., Python REPL).
  3. IDE – integrated tools (VS Code, PyCharm, Eclipse).
• Flowchart: Visual map of algorithm; uses start/stop, process, decision, input/output symbols. Useful in many fields (example: bank account opening process).

Data Analysis Basics

• Data = plural form of datum (Latin); spans numbers, text, audio, images, video.
• Storage growth: from kilobyte $(1\text{ KB}=1024\text{ B})$ to petabyte $(2^{50}\text{ B})$.
• Ackoff DIKW Hierarchy: Data → Information (answers what/where/when/who) → Knowledge (answers why) → Insight → Wisdom.
• Data-analysis cycle (Cote 2021):
  1. Descriptive – describe "as-is" (e.g., attendance spike in Nov–Dec).
  2. Diagnostic – explain "why" (high rainfall → illness).
  3. Predictive – forecast future (next Nov–Dec spike likely).
  4. Prescriptive – recommend optimal action (vitamin reminder + rain gear).
• Generic processing loop: Collection → Verification/Cleaning → Transformation → Modelling → Presentation; output can feed next cycle.

Implementation Guidance & Pedagogical Connections

• Use Technological Pedagogical Content Knowledge (TPACK) framework.
• Embed Higher Order Thinking Skills (HOTS) & Deep Learning strategies.
• Mix unplugged, plugged & online modalities for inclusivity.
• Encourage reflection: SWOT analysis of KKA adoption at school level.
• Ensure infrastructure readiness: devices, internet, teacher skill, technical support.

Ethical, Philosophical & Practical Implications

• Inclusivity: narrow digital divide; offer financial & accessibility support.
• Sustainability: account for energy use & e-waste; promote green computing.
• Cultural preservation: leverage AI to archive & promote local heritage.
• Lifelong learning mindset: foster adaptability as technology evolves.

International Reference Frameworks & Resources

• AI4K12 (USA) – 5 Big Ideas framework.
• Learn AI Singapore – educator resources.
• CommonSense AI Literacy Lessons (grades 6-12).
• CS Unplugged, Code.org, Bebras for CT activities.

Conclusion / Way Forward

• Module equips teachers with conceptual & practical mastery of CT, DL, Coding & AI.
• Teachers expected to continually enhance skills, design HOTS-oriented KKA lessons, and employ TPACK & Deep Learning frameworks (to be elaborated in Module 5).
• Ultimate goal: Learners become ethical, creative, competent digital citizens and tech innovators who can compete globally while upholding national values.

Select Numerical / Formula Highlights

• Turing Test conceptual; modulo example: $(8 + 3) \bmod 10 = 1$.
• Storage escalation: $1\text{ KB}=1024\text{ B},\; 1\text{ PB}=2^{50}\text{ B}$.

Key References (abridged)

• Russell & Norvig 2010 – AI: A Modern Approach.
• Wing 2006 – Computational Thinking.
• UNESCO 2021/2024 – AI in Education & AI Literacy Framework.
• Goodfellow et al. 2016 – Deep Learning.
• Ackoff 1989 – DIKW hierarchy.
  (See full list in original module p. 47-48 for complete citation details.)