Research-informed design. Practical AI adoption.
Stride draws on behavioral economics, gamification, and habit research to design practical AI learning and support.
READS Assessment
- 1.Trailblazer
- 83/100
- 2.Guide
- 67/100
- 3.Connector
- 50/100
- 4.Explorer
- 92/100
- 5.Champion
- 42/100
- 6.Innovator
- 58/100
Sample preview
Take the assessment. Get a journey built around you.
An organization-configured assessment offers a starting point for reflection and support. Available formats and retakes depend on configuration.
Take the READS Assessment
Assessment length varies by format and organization settings. Answer in your own terms; retakes are available through the configured experience.
Reflect on your starting point
Your answers produce a HEXAD-informed dominant motivational preference, self-reported readiness signals, and a journey-stage label. Stride's adaptation uses heuristics and is not an independently validated prediction.
Get a journey built around that read
Eligible coaching and support can use assessment and activity signals. Learning recommendations match role, skills, goals, difficulty, industry, and tools while approved catalog content remains discoverable.
Three perspectives on adoption
Design choices aim to make first steps approachable, practice engaging, and useful routines easier to repeat. Outcomes vary by person and rollout.
Support a first step.
Behavioral economics studies how framing, friction, and context affect decisions. Stride uses clear next steps and prompts to make practice easier to approach.
Design aim · First actionMake practice engaging.
Quests, challenges, and visible progress offer reasons to participate again. Engagement and activity rewards are distinct from evidence of skill mastery.
Design aim · Participation and repetitionSupport a useful routine.
Habit research examines repetition in stable contexts. Stride encourages practical experiments with familiar routines; lasting habit formation requires evidence over time.
Design aim · Repeat useful practiceSignals behind eligible support
Assessment-derived preferences and self-reported readiness are distinct from ongoing participation signals.
The Fogg Behavior Model describes motivation, ability, and a prompt coming together. Stride's readiness quadrants use assessment answers; activity signals such as completions and streaks support separate guidance.
Higher self-reported motivation and ability. Support can emphasize autonomy and available practice; permissions determine feature access.
Higher self-reported motivation, with ability still developing. Structured learning offers a place to practice.
Higher self-reported ability, with lower motivation. Relevant examples and optional prompts can support exploration.
Lower self-reported motivation and ability. Small, optional practice steps offer a manageable starting point.
The assessment summarizes one dominant HEXAD-informed preference from six patterns. People can relate to several patterns; the result is a guide for support rather than a fixed identity.
Trailblazer
Personal mastery and tangible progress
Guide
Helping others succeed
Connector
Social connection and team success
Explorer
Discovery and freedom
Champion
Winning and recognition
Innovator
Creating something new
Sample preview
A sample interface showing a quest path and archetype label, rather than an assessment result for this visitor.
Six motivational preferences to explore
These HEXAD-informed patterns describe possible preferences. Stride's shortened assessment and tie-break heuristics have not been independently validated as a predictive instrument.
Trailblazer
Personal mastery and tangible progress
You thrive on learning and mastering new skills. AI is your next frontier to conquer.
Guide
Helping others succeed
You see AI as a way to amplify your impact and help those around you succeed.
Connector
Social connection and team success
You learn best with others and see AI as a shared journey worth taking together.
Explorer
Discovery and freedom
You value creative freedom and see AI as an expansive toolkit for self-directed exploration.
Champion
Winning and recognition
You love a good challenge. Leaderboards and milestones fuel your drive to master AI.
Innovator
Creating something new
You push boundaries and experiment boldly. AI is your lab for transformative ideas.
Mechanics and their design rationale
The principles below explain why Stride uses these mechanics. They describe research-informed intentions, rather than proof that each mechanic produces an outcome in Stride.
XP & Levels
Earn XP and levels as you complete activities and build momentum.
Visible progress can make the next step easier to recognize and give participation a sense of continuity.
Streaks
Momentum you can see, with a gentle nudge before it breaks.
A visible streak can encourage repetition. Its effect varies; maintaining it is not proof of a lasting habit.
BUX
Stride's in-app currency, earned for completing quests and helping others.
Optional rewards offer a reason to return and choose how to recognize participation.
Badge Marketplace
Spend BUX on badges, avatar flair, and recognition that's actually earned.
Visible recognition marks participation and achievement criteria, rather than independently demonstrated competence.
Quests & Quest Paths
Bite-sized missions that turn AI adoption into a guided path, not a mandate.
A clear next step lowers the effort of the first action, and the tenth.
Weekly Challenges
Time-boxed team challenges with a submission feed teammates like and reply to.
Voluntary sharing can support encouragement and idea exchange. Its effect depends on people and context.
Prestige
Hit the level cap and reset with a mark that shows you've been here before.
A reason to keep going once the first ladder is climbed, instead of plateauing.
Achievement Badges
Hit a streak length, a level, or a challenge count and you're awarded a badge on your profile and your collection. Separate from the marketplace: these can't be bought, only earned.
Earned badges record defined activity milestones. They do not certify professional competence.
Nudges
Configured in-app prompts can draw attention to a next step or an at-risk streak. Managers can also send preset support messages to assigned reports.
A relevant prompt can help someone notice a possible next action. It does not guarantee completion or habit change.
Research behind the design
Explore your starting point.
Use the configured assessment to reflect on preferences and readiness, then explore practical ways to build AI into your work.