International Olympiad in Artificial Intelligence
| Conducted by | International Olympiad in Artificial Intelligence (IOAI) International Board (administered by the LERAI Foundation, a non-profit organisation registered in Bulgaria) |
|---|---|
| Exam type | Python coding contest: two individual contests of 3 tasks, 6 hours each; plus a Team Challenge.Source: Contest Rules for IOAI 2026 (Version 4) and Technical Appendix, sections 2.1 and 2.2 (pp. 2-3), section 3 (p. 8) |
| Field | Artificial Intelligence |
| Who can apply | Contestants must be 20 years old or younger on 1 July of the year of the edition and normally enrolled in full-time primary or secondary education (or, if home-schooled, working toward a high-school credential and not holding one). They are selected through country-wide competitions in AI, informatics, mathematics, linguistics or a related field.Source: Regulations of the International Olympiad in Artificial Intelligence (IOAI), section 8.3.1 (p. 41); also Contest Rules section 1.2 |
| Age / class | 20 years old or younger on 1 July of the year of the IOAI edition; high-school (primary or secondary education) students.Source: Regulations of the International Olympiad in Artificial Intelligence (IOAI), section 8.3.1 (p. 41) |
| How often | Annual global competition week, held in a different host country each edition (2024 Bulgaria, 2025 China, 2026 Kazakhstan are listed on the site).Source: Regulations of the International Olympiad in Artificial Intelligence (IOAI), section 3.1 (p. 8); editions listed on the home page |
| Stages | National IOAI stages (Ignite, Orient, Advance, Immerse) lead to the international event, which has an Individual Contest, a Team Challenge and a GAITE Contest.Source: Regulations of the International Olympiad in Artificial Intelligence (IOAI), section 6.2 (p. 13); Contest Rules intro and section 2 (pp. 1-2) |
| How to apply | Each Accredited Country or Territory may send one team of up to four contestants and a team leader; the Board decides each year whether a second team is allowed.Source: Regulations of the International Olympiad in Artificial Intelligence (IOAI), section 8.2.1 (pp. 40-41) |
| Result / certificate | Medals are based only on Individual Contest scores: gold for at least one-twelfth of contestants, gold or silver for at least one-quarter, and gold, silver or bronze for at least one-half. Honourable Mentions and trophies for the top three also exist.Source: Contest Rules for IOAI 2026 (Version 4) and Technical Appendix, section 5.1 (pp. 9-10) |
| Talent category | Olympiads |
| Pathway | Country-wide national AI olympiad (National IOAI) > IOAI |
| Recognition | Official / statutory body |
| Level | International |
| Achievement | Gold, silver and bronze medals, Honourable Mentions, and First, Second and Third Place Trophies for the top three individual contestants; Team Challenge trophies for the top three teams. |
Individual Contest: an at-home set of 3 problems for education (not counted in results), then Individual Contest 1 and Individual Contest 2, each with 3 tasks and six hours. Contestants work in Python, mainly in a Jupyter Notebook, with torch and scikit-learn; tasks are designed in accordance with the IOAI Syllabus. Medals depend only on the Individual Contest. Marks per task are not stated in the sections read.
Read from: Contest Rules for IOAI 2026 (Version 4) and Technical Appendix, sections 2, 2.1 and 2.2, pp. 2-3
Python Basics (Loops, Functions, etc.) (Practice); NumPy and Pandas for Data Handling (Practice); Matplotlib and Seaborn for Visualization (Practice); Scikit-learn for ML (Practice); PyTorch Basics (Practice); Tensor (Multi-dimensional Array) Manipulation (Practice); Training Models on CPU and GPU (Practice)
Read from: IOAI 2026 Syllabus - International Olympiad in Artificial Intelligence, p. 2, section 1, table row group 'Programming Fundamentals'Linear Regression (Both); Logistic Regression (Both); L1 & L2 Regularization (Both); K-Nearest Neighbors (K-NN) (Both); Decision Trees (Both); Model Ensembles (Gradient Boosting, Bagging, Random Forest) (Practice); Support Vector Machines (SVM) (Both)
Read from: IOAI 2026 Syllabus - International Olympiad in Artificial Intelligence, p. 2, section 1, table row group 'Supervised Learning'K-Means Clustering (Both); Principal Component Analysis (PCA) (Both); t-SNE, UMAP (Practice); DBSCAN, Hierarchical & Spectral Clustering (Practice)
Read from: IOAI 2026 Syllabus - International Olympiad in Artificial Intelligence, p. 2, section 1, table row group 'Unsupervised Learning'Model Evaluation Metrics (Accuracy, Precision, Recall, F1-Score, etc.) (Both); Underfitting, Overfitting (Theory); Hyperparameter Tuning (Practice); Cross-Validation (Practice); Confusion Matrix and ROC Curves (Both); Feature Engineering * (Practice); Data Processing ** (Practice). * Feature Engineering involves transforming raw, potentially high-dimensional data, categorical data, time series, or ragged data into a compact set of informative features. Techniques involve sliding windows, pooling operations, one-hot encoding, statistical moment-based features (average, standard deviation), PCA and neural-network-based embeddings. ** Data Processing concerns the handling of missing data and irregular data, including in sequence modeling settings. Techniques involve basic imputation strategies (mean/median/forward-fill for sequences) and padding for variable-length sequences. Covered here are also normalization and standardization techniques, train/validation/test splitting strategies, basic data augmentation (flipping, cropping, noise addition), tokenization and vocabulary building for text and audio and patching for images.
Read from: IOAI 2026 Syllabus - International Olympiad in Artificial Intelligence, p. 2, section 1, table row group 'Data Science Fundamentals', with the two footnotes below the tablePerceptron Basics (Both); Gradient Descent (Both); Backpropagation (Both); Activation Functions (ReLU, Sigmoid, Tanh) (Both); Loss Functions (MSE, MAE, Cross Entropy, etc.) (Both)
Read from: IOAI 2026 Syllabus - International Olympiad in Artificial Intelligence, p. 2, section 2, table row group 'Neural Networks'Multi-Layer Perceptrons (MLP) (Both); Data Embeddings (text, image, audio) (Both); Pooling Techniques (Max, Average) (Both); Attention Mechanism (Both); Transformers (theory needed only for text and image) (Both); Autoencoders (Practice); SGD, Mini-Batch Gradient Descent (Both); Momentum Methods (Adam, AdamW) (Practice); Convergence and Learning Rates (Practice); Regularization: Dropout, Early Stopping, Weight Decay (Practice); Weight Initialization (Practice); Batch Normalization (Practice); Model Finetuning (full and parameter-efficient) (Practice)
Read from: IOAI 2026 Syllabus - International Olympiad in Artificial Intelligence, pp. 2-3, section 2, table row group 'Deep Learning'Convolutional Layers (Both); Image Classification (Practice); Object Detection (YOLO, SSD, DERT) (Practice); Image Segmentation (U-Net) (Practice); Pre-trained Vision Encoders (e.g. ResNet) (Practice); Image Augmentation (Practice); Generating Images with GANs (Practice); Self-Supervised Learning for Vision (Practice); Vision-text encoders (e.g. CLIP) (Practice); Diffusion Models (Practice)
Read from: IOAI 2026 Syllabus - International Olympiad in Artificial Intelligence, p. 3, section 3, table row group 'Fundamentals'Text Classification (Both); Pre-trained Text Encoders (e.g. BERT) (Both); Language Modeling (Practice); Encoder-Decoder Models (e.g. for Machine Translation or Vision-Language Modeling) (Practice); Pre-trained Language Models (open-source and API-based ones) (Practice)
Read from: IOAI 2026 Syllabus - International Olympiad in Artificial Intelligence, p. 3, section 4, table row group 'NLP'. The table cells are interleaved in the PDF text, so the split between NLP and Audio Processing follows the subtopic namesPre-trained Audio Encoders: HuBERT (Practice); Audio Models: Qwen-Audio, Whisper, Voxtral (Practice). Note printed under the section: The data can be text, tabular, image, audio, video, and time-series, and should be processed with the methods above.
Read from: IOAI 2026 Syllabus - International Olympiad in Artificial Intelligence, p. 3, section 4, table row group 'Audio Processing'Tell us which exam you are aiming for and how we can help - we will get back to you.
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