adaptive testing:Gemini 2.5 Pro Experimental - chunhualiao/public-docs GitHub Wiki

adaptive testing

Okay, building an adaptive testing platform like EF SET for any subject using today's AI is an ambitious but increasingly feasible goal. Here's a breakdown of how you could approach its development, integrating insights from AI capabilities and adaptive testing principles:

1. Core Concept: Adaptive Testing with AI

The fundamental idea is to create a test that adjusts its difficulty based on the user's performance in real-time. Unlike static tests where everyone gets the same questions, an adaptive test aims to quickly pinpoint a user's proficiency level by presenting questions that are neither too easy nor too hard for them. AI, particularly Large Language Models (LLMs), plays a crucial role in generating the diverse and dynamically difficult questions needed for any subject.

2. Key Components and Development Steps:

  • a. Platform (Frontend & Backend):

    • Technology: Standard web development technologies (HTML, CSS, JavaScript for the frontend; a backend framework like Python/Django/Flask, Node.js/Express, Ruby on Rails; a database like PostgreSQL, MySQL, MongoDB).
    • Functionality: User registration/login, subject selection, test interface (displaying questions, receiving answers), results display (score, rank), user profile management.
    • Accessibility: Ensure the interface works smoothly on both desktop and mobile browsers.

  • b. AI Integration - Question Generation (LLM):

    • Choose an LLM: Select a powerful LLM API known for broad knowledge and text generation capabilities (e.g., Google's Gemini API, OpenAI's GPT series, Anthropic's Claude). Consider factors like cost, API limits, subject coverage, and ease of integration.
    • Prompt Engineering: This is critical. You need to design specific instructions (prompts) for the LLM to:
      • Generate questions on a specific subject.
      • Generate questions at a requested difficulty level (e.g., beginner, intermediate, advanced, or perhaps on a numerical scale).
      • Generate specific question types (e.g., multiple-choice with distractors, true/false, fill-in-the-blank, short answer).
      • Provide the correct answer for automated grading.
      • Example Prompt Idea: "Generate an intermediate-level multiple-choice question about 'inventory management techniques' in supply chain management. Provide 4 answer options (1 correct, 3 plausible distractors) and indicate the correct answer."
    • Fine-tuning (Optional): For highly specialized subjects or improved question quality, you might consider fine-tuning an LLM on domain-specific educational materials, although this adds complexity and cost.

  • c. Adaptive Engine (The Core Logic):

    • Ability Estimation: Implement an algorithm to estimate the user's knowledge level ($\theta$) as they answer questions. This is often based on Item Response Theory (IRT).
    • IRT Principles: IRT models the probability of a user answering a question correctly based on their ability ($\theta$) and the question's characteristics (primarily difficulty $b_i$, but potentially also discrimination $a_i$ and guessing $c_i$).
      • Difficulty ($b_i$): How hard the question is. In IRT, it's the ability level ($\theta$) at which a user has a 50% chance of answering correctly (for simpler models).
      • Discrimination ($a_i$): How well the question differentiates between users of different ability levels.
    • Algorithm Flow:
      1. Start: Begin with a question of medium difficulty (e.g., ask the LLM for a difficulty level 3 out of 5).
      2. Respond: User answers the question.
      3. Evaluate: Check if the answer is correct. (For short answers, you might even use the LLM again to evaluate correctness, comparing the user's response to the LLM-provided correct answer, though this needs careful validation).
      4. Update Ability ($\theta$): Use the response (correct/incorrect) and the estimated difficulty of the question just answered to update the estimate of the user's ability ($\theta$). IRT provides mathematical formulas for this.
      5. Select Next Question: Choose the difficulty for the next question. The ideal next question is one whose difficulty ($b_i$) is close to the current estimate of the user's ability ($\theta$), as this provides the most information to refine the ability estimate. Instruct the LLM to generate a question at this target difficulty level.
      6. Repeat: Continue steps 2-5 until a stopping criterion is met (e.g., a fixed number of questions answered, or the ability estimate $\theta$ stabilizes with sufficient precision).
    • Challenge: A key challenge with using LLMs dynamically is that you don't have pre-calibrated item difficulties like in traditional IRT banks. You'll need to rely on the LLM's ability to generate questions at requested difficulty levels, or develop methods to estimate the difficulty of generated questions on the fly.

  • d. User Ranking:

    • Data Storage: Store the final estimated ability score ($\theta$) for each user per subject test.
    • Calculation: When a user completes a test, compare their score ($\theta$) to the distribution of scores for all other users who have taken a test in that same subject.
    • Display: Show the user their rank, often as a percentile (e.g., "You scored better than 85% of users in Supply Chain Management").

3. Technologies & Tools:

  • LLM APIs: Google AI (Gemini), OpenAI API (GPT models), Anthropic API (Claude), potentially open-source models hosted via platforms like Hugging Face.
  • Web Frameworks: React, Angular, Vue (frontend); Django, Flask, Node.js, Rails (backend).
  • Databases: PostgreSQL, MySQL, MongoDB.
  • Libraries: Potentially libraries for statistical calculations (if implementing complex IRT models), API request libraries.

4. Challenges & Considerations:

  • Question Quality: Ensuring LLM-generated questions are accurate, relevant, unambiguous, and truly match the requested difficulty. Requires rigorous prompt engineering and potentially human review/curation, especially initially.
  • Difficulty Calibration: Reliably controlling and assessing the difficulty of dynamically generated questions is non-trivial.
  • Bias: LLMs can reflect biases present in their training data. This could lead to unfair questions or evaluations. Careful monitoring and potentially bias mitigation techniques are needed.
  • Evaluation: Automatically evaluating free-text answers using an LLM can be complex and may not always be accurate.
  • Cost: LLM API calls are typically priced per token (amount of text processed), so generating many questions can become expensive. Caching previously generated questions could help.
  • Cheating: Implement measures to discourage users from simply looking up answers (e.g., time limits, monitoring copy-paste actions, potentially using browser lockdown tools for stricter assessments).
  • Subject Specificity: While LLMs have broad knowledge, generating very deep or niche questions might require more specific prompting or fine-tuning.

Building this system requires a combination of web development expertise, careful AI/LLM integration and prompt engineering, and an understanding of psychometric principles like adaptive testing and IRT. Starting with a simpler heuristic approach to difficulty adjustment before implementing full IRT might be a practical strategy.