AI for tasks and activities

Generative AI can help learning designers create tasks and activities by suggesting formats, drafting instructions, generating examples, and varying the complexity or context of learner engagement. This can be useful during early design and iteration, especially where multiple practice opportunities are needed.

Where AI can help

AI may be useful for generating:

discussion prompts short practice tasks scenario-based activities reflection questions role-play setups brainstorming activities formative checks for understanding alternate versions of a task for different contexts or levels

It can also help a designer quickly produce several options and then choose the one that best aligns with the learning purpose.

Good practice

When using AI for tasks and activities:

Start from the learning objective
Define what learners should know, do, or demonstrate. The task should exist to support that goal.

Specify the type of learner action needed
For example, should the learner discuss, analyse, compare, create, practice, reflect, or perform?

Check authenticity
Ensure the task resembles the kind of thinking or performance expected in the actual learning context.

Review cognitive load
AI can generate tasks that are either too easy, too broad, or too demanding. Adjust the scope and scaffolding.

Ensure practical usability
The activity needs clear instructions, reasonable timing, and suitable outputs.

Risks and limitations

AI-generated tasks may:

look active but have weak alignment to the objective duplicate surface-level engagement without deeper learning value create unrealistic or overly polished workplace scenarios miss the need for scaffolding or prior knowledge produce repetitive formats across a course

These risks mean the designer should review tasks for both learning value and practical teachability.

Example uses

Example 1: Generating discussion options

A designer wants an end-of-page discussion question for a topic on ethical decision-making. AI can generate several prompts at different levels of complexity, from simple opinion-sharing to evidence-based evaluation.

Example 2: Varying practice activities

A designer has one useful task format and wants three more versions using different contexts or examples. AI can propose alternate scenarios while the designer checks that each one still aligns with the same objective.

Practical guidance

AI is strongest when used to support task design by helping with:

ideation variation first drafts rewording instructions generating examples or prompts

It is weaker when left to define the educational strategy on its own.

Use AI to speed up the design of tasks and activities, but ensure the final activities are aligned, purposeful, and feasible for the actual learners.