Learning Experience Design

Equity

Grow the Learner

Instrinsic Reinforcement

Feedback

Choice

Modeling

Acknowledge completion of learning.

Connect content to learner's context.

Organize content in a way that promotes self-efficacy.

Inquiry

Satisfaction

Learner Experience Design

Application to Technology

Examples

Get the attention of learners by arousing perceptions or inquiry.

GBL: Give embedded and explicit feedback.

Meaning

Components

GBL: End with a clear appraisal of learning.

GBL: Incorporate branching.

Attention

Active Participation

GBL: Create a compelling story.

Relevance

Confidence

The most satisfying games (and learning experiences in general) have a defined end or goalpost for success. In game-based learning, players should receive explicit feedback at the end of the game letting them know that they finished the game and whether they achieved the game's learning objectives. Games should also be replayable so that learners who did not achieve learning goals can redo the game. This accounts for varying levels of proficiency and rates of learning among players.

Satisfaction: Game-Based Learning

End With a Clear Appraisal of Learning

To make the game truly relevant to learners, a game must include branching to allow players to make individual choices rather than simply being moved along in a game. This allows learners to apply what they already know and deepen that knowledge or explore an area that they don't know and fill a gap in knowledge.

Relevance: Game-Based Learning

Incorporate Branching

In game-based learning, developers and instructors must create a story that appeals to a wide range of learners. For example, some students may not be interested in learning about AI, but if the story of the game is engaging enough, they will still enjoy the learning experience. Developers must also account for players' previously obtained knowledge; if the game appears too simple from the beginning, they will never gain interest.

Attention: Game-Based Learning

Create a Compelling Story

Game designers and instructors can instill confidence in learners by giving players feedback through the game itself (i.e., a player who is unsuccessful in accomplishing a task in the game learns that their attempted application of knowledge was incorrect, while a player who is successful receives confirmation that they correctly applied their knowledge) and explicitly through pop-up bubbles or narration. This accounts for differing student abilities and rates of learning by encouraging all players to persist in the game with additional guidance.

Confidence: Game-Based Learning

Give Embedded and Explicit Feedback

To teach the benefits and harms of artificial intelligence, an instructor can make the content relevant by using game-based learning (using games like AI for Oceans and Survival of the Best Fit) to show real-world applications of AI programs. Alternatively, an instructor may show how they use AI themselves or show videos of others describing their AI use at work.

Relevance

Modeling

To teach the benefits and harms of artificial intelligence, an instructor can instill confidence in learners by assigning scaffolded assignments, such as: watching the instructor model AI use, practicing with AI, reading and reflecting upon opinion pieces about AI, analyzing a sample AI policy, and drafting their own AI policy. These incremental steps allow for building proficiency.

Confidence

Grow the Learners

To teach the benefits and harms of artificial intelligence, an instructor can encourage satisfaction by using game-based learning, which is intrinsically motivating because it is fun, or by incorporating students' final submissions into a classroom AI policy so that they can see their learning being applied in a real-world scenario.

Satisfaction

Instrinsic Reinforcement

To teach the benefits and harms of artificial intelligence, an instructor can make the content relevant by letting students choose individually which AI to work on as part of an assignment or collectively what kinds of professions they'd like to explore as part of a class lecture on AI in the workplace. Alternatively, game-based learning builds in choice by allowing players to make decisions that alter their course in the game.

Relevance

Choice

To teach the benefits and harms of artificial intelligence, an instructor can instill confidence in learners by asking students to draft their own AI policies for the classroom, which the class (with the instructor's guidance) then collaboratively synthesizes into an official classroom AI policy. This assignment would be meaningful to students because they would see their own learning reflected in a course documented adopted by the instructor themself.

Confidence

Meaningful Success

To teach the benefits and harms of artificial intelligence, an instructor can encourage satisfaction by assigning a challenging final assignment that requires original thought and creation, such as drafting an AI policy. Earlier assignments should be graded, but should not be weighed as heavily as the final assignment that requires creative thinking.

Satisfaction

Equity

To teach the benefits and harms of artificial intelligence, an instructor can capture learners' attention by asking learners questions about what authorship means and how they've seen technology do good or harm in their own lives.

Attention

Inquiry

To teach the benefits and harms of artificial intelligence, an instructor can capture learners' attention by beginning with having learners play an online game related to AI (such as AI for Oceans [https://studio.code.org/s/oceans] or Survival of the Best Fit [https://www.survivalofthebestfit.com], depending on their level) or directly experiment with AI, such as ChatGPT.

Attention

Active Participation