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JSSC Secondary Teacher Artificial Intelligence and Coding (Paper-I) Exam - Hindi200 questions
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JSSC Secondary Teacher Artificial Intelligence and Coding (Paper-I) Exam - Hindi200 questions
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JSSC Secondary Teacher (Artificial Intelligence and Coding) Syllabus 2026 Exam Pattern
JSSC Secondary Teacher (Artificial Intelligence and Coding) Syllabus 2026, Exam Pattern, Exam Date 2026
Detail Information about JSSC has published notification 2026 for the recruitment of Secondary Teacher (Artificial Intelligence and Coding) vacancies. Those Candidates who are Interested to the following vacancy and completed all Eligibility Criteria can read the Notification & Apply Online. In this page we provide the Complete Syllabus of this Recruitment with Latest Update Exam Pattern and the Exam Date also.
JSSC Secondary Teacher Syllabus 2026 - Overview
| Organization Name | Jharkhand Staff Selection Commission (JSSC) |
| Exam Name | Jharkhand Trained Secondary Teacher Combined Competitive Examination 2026 |
| Post Name | Secondary Teacher (Artificial Intelligence and Coding) |
| Vacancies | Notified Soon |
| Mode of Application | Online |
| Category | Syllabus and Exam Pattern |
| Selection Process | Written Exam (Main Examination: Paper I & II) |
| Official Website | https://jssc.jharkhand.gov.in |
JSSC Secondary Teacher (Artificial Intelligence and Coding) Syllabus 2026
General Studies :
1. Current Affairs - Scientific Progress, National/International Awards, Indian Languages, Books, Scripts, Capitals, Currency, Sports, and Important Events.
2. India's History, Culture, Geography, Environment, Economic Scenario, Independence Movement, Indian Agriculture, and Major Natural Resources,
3. Indian Constitution and Governance, the Country's Political System, Panchayati Raj. Community Development, Five-Year Plans.
General Knowledge of Jharkhand
Information about the geographical and political situation of Jharkhand state.
1. Jharkhand's Geography
2. History
3. Civilization and Culture
4. Language-Literature
5. Places
6. Minerals
7. Industries
8. Jharkhand's contribution to the National Movement
9. Development Plans
10. Sports personalities
11. Personalities
12. Civic achievements
13. National and International importance-related topics.
General Science
1. Physics
2. Chemistry
3. Biology
General Mathematics:
1. Arithmetic
2. Primary algebra
3. Geometry
4. Trigonometry, and related to area measurements.
Mental Ability Test
1. Visual similarity and difference
2. Spatial visualization
3. Problem-solving
4. Analysis
5. Visual memory
6. Relationship concepts
7. Arithmetic reasoning.
8. Numerical series
9. Coded writing and interpretation, etc.
Basic knowledge of computers :
1. Microsoft Windows
2. Operating system
3. Generation of Computers
4. RAM, ROM
5. MS Office
6. Internet
Knowledge on Hindi & English Language :-
Hindi Language :
1. Unseen Passage Questions on Grammar on the following –
2. Idioms & Phrases.
3. Verb.
4. Reading Comprehension.
5. Articles.
6. Meanings.
7. Unseen Passages.
8. Sentence Corrections.
9. Error Spotting/Phrase Replacement.
10. Para Jumbles.
11. Word Formations.
12. Adjectives.
13. Synonyms
14. Antonyms
15. Sentence Rearrangement.
English Language
1. Unseen Passage Questions on Grammar including topics such as –
2. Samas, Sandhi, Verb, Adjective, Sets
3. Synonyms
4. Paragraphs
5. Proverbs
6. Antonyms
7. Phrase
Paper II: Artificial Intelligence and Coding :
1. Fundamentals of Artificial Intelligence :
Introduction to Artificial Intelligence- History and evolution of Al, Types of Al (Narrow/Weak Al vs General/Strong Al), Applications of Al in various domains, Ethical considerations in Al
Intelligent Agents- Agent architectures, Environment types, Rational agents, Performance measures
Problem-Solving and Search Problem formulation, Uninformed search strategies (BFS, DFS, UCS), Informed search (A", heuristic search), Constraint satisfaction problems
Knowledge Representation and Reasoning- Logic-based representation (propositional and predicate logic), Rule-based systems, Semantic networks and frames, Ontologies and knowledge graphs
2. Machine Learning and Deep Learning :
Machine Learning Fundamentals Types of learning (supervised, unsupervised, reinforcement), Training, validation, and testing, Overfitting and regularization, Evaluation metrics
Supervised Learning- Linear and logistic regression, Decision trees and random forests, Support vector machines, K-nearest neighbors
Unsupervised Learning- Clustering algorithms (K-means, hierarchical), Dimensionality reduction (PCA), Association rule learning, Anomaly detection
Neural Networks- Perceptrons and multilayer networks, Activation functions, Backpropagation, Optimization algorithms
Deep Learning Convolutional neural networks, Recurrent neural networks and LSTM, Transfer learning, Generative models (GANS, VAES)
Natural Language Processing- Text preprocessing techniques, Word embeddings (Word2Vec, GloVe), Sequence models for NLP, Transformers and attention mechanisms
Data Structures for Al- Graphs and their representations, Priority queues and heaps, Hash tables, Trees for search (BST, AVL, Red-Black)
Programming for Al (Python)- Python syntax and data structures, NumPy, Pandas for data manipulation, Matplotlib, Seaborn for visualization, Object-oriented programming in Python
Al Libraries and Frameworks- TensorFlow and Keras, PyTorch, Scikit-learn, NLTK and
3. Applications and Projects
Computer Vision-Image processing fundamentals, Object detection and recognition, Image segmentation, Face recognition and emotion detection
Robotics and Automation- Robot kinematics, Path planning algorithms, Sensor integration, Multi-agent systems
Al In Games- Game trees, Minimax algorithm and alpha-beta pruning, Monte Carlo methods, Reinforcement learning for games
Al Project Development- Project life cycle for Al applications, Data collection and preparation, Model selection and training, Evaluation and deployment
1. Fundamentals of Artificial Intelligence :
Introduction to Artificial Intelligence- History and evolution of Al, Types of Al (Narrow/Weak Al vs General/Strong Al), Applications of Al in various domains, Ethical considerations in Al
Intelligent Agents- Agent architectures, Environment types, Rational agents, Performance measures
Problem-Solving and Search Problem formulation, Uninformed search strategies (BFS, DFS, UCS), Informed search (A", heuristic search), Constraint satisfaction problems
Knowledge Representation and Reasoning- Logic-based representation (propositional and predicate logic), Rule-based systems, Semantic networks and frames, Ontologies and knowledge graphs
2. Machine Learning and Deep Learning :
Machine Learning Fundamentals Types of learning (supervised, unsupervised, reinforcement), Training, validation, and testing, Overfitting and regularization, Evaluation metrics
Supervised Learning- Linear and logistic regression, Decision trees and random forests, Support vector machines, K-nearest neighbors
Unsupervised Learning- Clustering algorithms (K-means, hierarchical), Dimensionality reduction (PCA), Association rule learning, Anomaly detection
Neural Networks- Perceptrons and multilayer networks, Activation functions, Backpropagation, Optimization algorithms
Deep Learning Convolutional neural networks, Recurrent neural networks and LSTM, Transfer learning, Generative models (GANS, VAES)
Natural Language Processing- Text preprocessing techniques, Word embeddings (Word2Vec, GloVe), Sequence models for NLP, Transformers and attention mechanisms
Data Structures for Al- Graphs and their representations, Priority queues and heaps, Hash tables, Trees for search (BST, AVL, Red-Black)
Programming for Al (Python)- Python syntax and data structures, NumPy, Pandas for data manipulation, Matplotlib, Seaborn for visualization, Object-oriented programming in Python
Al Libraries and Frameworks- TensorFlow and Keras, PyTorch, Scikit-learn, NLTK and
3. Applications and Projects
Computer Vision-Image processing fundamentals, Object detection and recognition, Image segmentation, Face recognition and emotion detection
Robotics and Automation- Robot kinematics, Path planning algorithms, Sensor integration, Multi-agent systems
Al In Games- Game trees, Minimax algorithm and alpha-beta pruning, Monte Carlo methods, Reinforcement learning for games
Al Project Development- Project life cycle for Al applications, Data collection and preparation, Model selection and training, Evaluation and deployment
JSSC Secondary Teacher (Artificial Intelligence and Coding) Exam Pattern 2026
| Paper | Subject | No.of Question | Marks | Duration |
| Paper 1 | General Knowledge (Including gk of Computer Operation) | 100 | 100 | 3 Hour |
| Hindi and English Language | 100 | 100 | ||
| Paper 2 | Subject Related Question | 150 | 300 | 3 Hour |
| Total | 350 | 500 | 6 Hour |
Exam Date 2026 : Notified Soon
Starting Date of Application Form : Notified Soon
Last Date of Application Form : Notified Soon
FAQs
Jssc Secondary Teacher (artificial Intelligence and Coding) 2026 Exam Syllabus Frequently Asked Questions (FAQ's)
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