Best Practices

Best Practices for Assess for Learning

Pro Tip: For best results, define detailed rubrics, model solutions, objectives, and aligned pedagogy within the Learning Journey Data Model (LDM). This maximizes the AI’s ability to provide targeted suggestions and mark with consistent reliability.

  1. Comprehensive Rubric Design

    • The AI can only mark effectively if your rubrics are thorough. Provide clear performance levels, detailed descriptions, and weights where applicable.

  2. Model Solutions for Complex Tasks

    • For essays or open-ended tasks, adding a Model Solution gives the AI a reference point to compare the learner’s work against an ideal.

  3. Objective Alignment

    • Ensure each assessment or module references the Objectives it aims to measure. This helps the AI provide more context-based feedback (e.g., “Level 4: Analyzing,” “Level 6: Creating”).

  4. Leverage Pedagogies

    • If you adopt Bloom’s or SOLO, the AI references those frameworks to classify and respond to learner queries at the right cognitive level.

  5. Keep LDM Objects Updated

    • Regularly revise Modules, Rubrics, and Objectives to reflect evolving course content. The AI relies on accurate data to provide correct insights.

  6. Encourage Learner Conversations

    • Assess for Learning excels when learners actually engage in two-way conversations about their performance. Remind them to ask follow-up questions and explore reasons behind the AI’s marking.

  7. Monitor & Validate

    • While the AI can automate much of the process, instructors should spot-check results—especially for borderline or high-stakes scenarios. Over time, the AI “learns” from consistent data and refined rubrics, improving accuracy.

😎 Enjoy!

Explore Tutor for Learning. Built for Agentforce, Tutor is a powerful extension to our Assess for Learning agent, designed to enhance the assessment experience by providing the learner with an AI interface into their assessment data.

PreviousMarkingNextTutor for Learning

Last updated