Activities, Learning, and Content

This section provides a detailed look at the foundational elements of AWorld Lab's gamification system: the activities users perform, the content they interact with, and the learning experiences that drive engagement. For the architectural overview of the Activity Plugin Layer and Catalog Layer, refer to the platform architecture documentation. For how activities feed into missions and rewards, see the missions and progression documentation.

Activities: Trackable Digital Actions

Activities are the core building blocks of AWorld Lab's gamification system. Each activity represents a Tractable Digital Activity (TDA) — a trackable action that feeds into the platform's engagement mechanics.

Activity Model and Origin Types

Every activity in the system is defined by a unique identifier, a name, and an origin type that determines how it was created:

Catalog: pre-built activities from AWorld Lab's validated catalog, ready for immediate use. Catalog activities can be synchronized automatically to receive updates.

Custom: activities defined by the client to match their specific business context and user interactions.

All activities support multi-language configuration, allowing clients to deliver localized experiences across different markets.

Activity Logging and Outcome Tracking

Every time a user performs an activity, the system creates an Activity Log entry that captures:

Outcome: whether the action was completed successfully or not.

Value: a numeric amount representing the magnitude of the action (defaulting to 1).

Tags: associated labels for categorization and filtering.

Timestamp: when the action was completed.

Activity logs serve as the primary trigger for all downstream gamification mechanics. When a log is created, the platform's event system automatically evaluates missions, reward rules, and streaks — turning each user action into an engagement opportunity.

Custom Activities and Third-Party Event Integration

Beyond standard activities, AWorld Lab allows clients to define custom activities tailored to their specific needs. The system can track a wide variety of events, including:

user actions within client digital touchpoints, such as posting content or leaving reviews;

interactions with external devices or platforms, like QR code scans;

any other configurable event that can trigger gamification dynamics.

Custom activities are registered through the API via Server-to-Server (S2S) integration, enabling clients to log actions on behalf of their users from any backend system.

Learning Paths

Learning Paths (LPs) are the primary content model in AWorld Lab, offering linear microlearning experiences that guide users through structured educational or engagement journeys. Learning Paths replace the legacy content model and provide significantly more flexibility and control.

Structure and Composition

Each Learning Path is defined by:

Title and description: metadata that describes the learning experience.

Cover image: visual identity for the path.

Estimated duration: expected time to complete, in minutes.

Origin type: how the content was created — Catalog (pre-built), AI (generated by AWorld Lab's AI system), or Custom (client-defined).

Learning Path Items

A Learning Path contains an ordered list of items, which can be of different types:

Slides: textual or media content for information delivery.

Quizzes: interactive questions to assess understanding.

Learning Groups: sub-sections that organize related items together.

Activities: trackable actions embedded within the learning flow.

This heterogeneous structure allows clients to create rich, varied learning experiences that combine informational content with interactive assessments and practical activities.

Assignment and Visibility

Learning Paths are delivered to users through Learning Path Assignments, which control when and how users access content:

Visibility: assignments can be Unlocked (immediately accessible) or Locked (visible but not yet accessible, awaiting an unlock trigger).

States: each assignment transitions through Pending (before start date), Active (within the timeframe), and Ended (after the timeframe expires).

Timeframes: assignments support Permanent (no end date), Range (specific start and end dates), and Recurring (repeating periods) configurations.

This model enables scenarios such as releasing content on a schedule, gating access behind prerequisites, or creating time-limited learning campaigns.

Assignment Rules: Automated Assignment and Unlocking

AWorld Lab provides a powerful rules engine for automating Learning Path delivery:

Assign rules automatically create assignments when users meet specific conditions, distributing Learning Paths from a configurable pool.

Unlock rules change the visibility of existing assignments from Locked to Unlocked when conditions are met — for example, unlocking an advanced path upon completing a prerequisite.

Rules operate in different assignment modes:

Lazy: assignments are created on-demand when the user browses available content, if they match the rule's conditions.

Event: assignments are triggered in real time by user actions (such as completing another Learning Path or earning a specific achievement). Combined with recurring timeframes, this also enables scheduled delivery at predetermined intervals.

Custom Completion Rules

Each Learning Path supports custom rules that define how completion, outcome, and start conditions are evaluated:

Completion rule: determines when the path is considered complete (e.g., "complete at least 3 of 5 items" or "complete all items in order").

Outcome rule: determines whether the overall result is a success or failure (e.g., "success if all quizzes passed").

Start rule: determines when the path is considered started.

These rules use a flexible expression language that allows clients to define complex conditions without code changes, adapting the learning logic to their specific requirements.

Progress Tracking

The system tracks detailed progress for each user across every Learning Path:

Progress states: Start, In Progress, and Complete.

Outcome: Success or Fail, determined by the configured outcome rule.

Per-item tracking: progress is recorded for each individual item within the path.

Current position: the system remembers where the user left off, enabling seamless continuation.

Learning Groups

Learning Groups are sub-sections within Learning Paths that organize related content items together. They function as self-contained units with their own completion logic.

Group Types and Composition

Learning Groups come in three types:

Story: a narrative sequence of slides with an optional closing quiz — the standard microlearning format.

Test: a sequence focused on assessment, primarily containing quizzes.

Custom: a flexible format combining any mix of content types.

Each group can contain slides, quizzes, and other content items, and supports the same origin types as Learning Paths: Catalog, AI, and Custom.

Completion Rules and Progress Tracking

Learning Groups have independent completion and outcome rules, evaluated separately from the parent Learning Path. This means a group can define its own success criteria — for instance, requiring all quizzes to be answered correctly, even if the overall Learning Path allows partial completion.

The system tracks group-level progress and supports user actions such as bookmarking groups for later and sharing them.

Slides and Quizzes

Slides: Text and Media Content

Slides are the fundamental content unit for delivering information. Each slide supports two content types:

Text slides: presenting written content to inform, educate, or guide the user.

Media slides: displaying images, videos, or other media resources.

Slides can be created from the catalog (pre-built), generated by AI, or defined as custom content. The system tracks when each slide is viewed and completed.

Quizzes: Interactive Assessment

Quizzes provide interactive assessment within Learning Paths and as standalone activities. Each quiz consists of a single-choice question with one correct answer, designed to reinforce learning and boost engagement.

Key features include:

Difficulty levels: quizzes can be tagged with difficulty for adaptive experiences.

Placement configuration: quizzes can be positioned contextually within learning flows.

Detailed logging: every attempt is recorded, including the user's answer, the correct answer, and the outcome (success or fail).

Catalog, Custom, and AI-Generated Content

AWorld Lab provides an extensive catalog of preconfigured quizzes ready for immediate use. Clients can also create custom quizzes with content tailored to their target audience, or leverage AWorld Lab's AI content generation system to produce quizzes automatically based on specific topics and goals.

Mobility Assistant

If AWorld Lab is integrated into a mobile application, clients can activate the Mobility Assistant — a specialized module that tracks users' sustainable movements and transforms them into gamification actions.

Mobility Milestones

Mobility Milestones are daily achievement targets that reward users for sustainable transportation choices. The system supports:

Three metrics: minutes of sustainable travel, kilometers covered, and CO₂ avoided.

Multiple target levels: up to ten progressive target thresholds per metric, enabling tiered recognition — from beginner goals to advanced challenges.

Transport type tracking: detection of specific sustainable transport modes (public transit, cycling, walking).

Milestone redemption: reached milestones can be redeemed to trigger rewards within the gamification system.

Mobility Tracking and Sustainability Metrics

The underlying Mobility Tracking system records individual trips with detailed data:

Trip detection: automatic identification of travel mode and duration.

Distance and route: trip length and geographic path (GeoJSON).

Environmental impact: CO₂ emitted and CO₂ avoided compared to car travel.

Data quality: built-in flags for misdetection, ensuring tracking accuracy.

This data feeds into the broader gamification system through activity logs, contributing to missions, leaderboard rankings, and reward accumulation.

Content Origin Model

A cross-cutting design principle in AWorld Lab is the content origin model, which applies consistently across activities, Learning Paths, Learning Groups, slides, and quizzes.

Catalog, Custom, and AI Origins

Every content entity supports up to three origin types:

Catalog: pre-built, validated content from AWorld Lab's curated library. Ideal for rapid deployment.

Custom: client-created content tailored to specific needs and brand identity.

AI: content generated by AWorld Lab's AI system, enabling fast creation of Learning Paths, slides, and quizzes from client-provided topics and objectives.

Catalog Synchronization

Catalog-sourced content can be synchronized automatically with AWorld Lab's catalog, ensuring that clients benefit from updates, corrections, and improvements without manual intervention. Clients retain the option to disable synchronization and manage content independently.

The content origin model allows clients to start quickly with catalog content, accelerate creation with AI, and progressively customize the experience — all within the same unified system.

Activities, Learning, and Content

Activities: Trackable Digital Actions#

Activity Model and Origin Types#

Activity Logging and Outcome Tracking#

Custom Activities and Third-Party Event Integration#

Learning Paths#

Structure and Composition#

Learning Path Items#

Assignment and Visibility#

Assignment Rules: Automated Assignment and Unlocking#

Custom Completion Rules#

Progress Tracking#

Learning Groups#

Group Types and Composition#

Completion Rules and Progress Tracking#

Slides and Quizzes#

Slides: Text and Media Content#

Quizzes: Interactive Assessment#

Catalog, Custom, and AI-Generated Content#

Mobility Assistant#

Mobility Milestones#

Mobility Tracking and Sustainability Metrics#

Content Origin Model#

Catalog, Custom, and AI Origins#

Catalog Synchronization#