Plus Tutoring
Helping tutors make informed scheduling decisions
- Role
- Product Design Intern
- Timeline
- In progress
- Skills
- Scheduling & Calendar UX, Complex Workflow Design, AI-Assisted Design
- Team
- Brittany Jain (Design Mentee)Bill Guo (Design Manager)
Project Overview
Problem
Tutors are frequently asked to cover sessions at the last minute. However, the existing scheduling system uses hard restrictions that prevent tutors from signing up for sessions that overlap with existing commitments.
Goal
Design a scheduling experience that reduces accidental overlapping sign-ups while preserving flexibility for legitimate coverage situations. The solution needed to:
Prevent mistake-driven scheduling conflicts.
Support last-minute coverage requests.
Keep tutors informed of potential overlaps.
Research
Understanding the existing workflow
To understand how tutors and supervisors managed overlapping sessions, I reviewed current scheduling flows and analyzed common conflict scenarios. I found that overlapping sessions generally fell into two categories:
Intentional overlaps
Situations where tutors knowingly accepted overlapping commitments because coverage was urgently needed and/or the overlap was brief.
Unintentional overlaps
Situations where tutors overlooked scheduling conflicts and later submitted call-off requests after realizing they could not attend both sessions.
Opportunity Statement
“How might we help tutors make informed scheduling decisions while preserving flexibility for legitimate coverage scenarios?”
Strategy
Reframing the experience
The existing system treated all scheduling conflicts the same way: by blocking tutors from signing up. However, our research revealed that not all overlaps carry the same level of risk.
A five-minute transition between sessions is fundamentally different from a forty-five-minute scheduling collision. Likewise, a tutor covering a single fill-in session faces a different decision than someone signing up for multiple recurring commitments.
Rather than designing around rigid restrictions, I focused on creating a system that helps tutors understand conflicts and make informed decisions.
Design principles
Make conflicts visible
Don't hide overlaps behind system rules. Surface conflicts early in the workflow through clear visual indicators and contextual information so tutors understand potential issues before committing.
Quantify severity
Not all conflicts are equal. The experience should clearly communicate the difference between a minor 5-minute transition, a moderate overlap, and a major conflict involving multiple sessions. Quantifying overlap duration helps tutors assess risk.
Support decision-making
Warnings alone don't solve the problem. The system should provide enough context for tutors to understand trade-offs and make informed decisions while maintaining flexibility for legitimate coverage scenarios.
Mapping the conflict landscape
To understand the full scope of the problem, I mapped every scheduling scenario involving:
- Recurring sessions
- One-time fill-ins
- Multiple session selections
- Mixed recurring and one-time conflicts
This resulted in more than 18 unique overlap scenarios. Rather than designing a unique solution for every edge case, I looked for patterns that could be consolidated into a smaller set of reusable experiences.
From 18 scenarios to 6 reusable patterns
18 scenarios
6 patterns
Creating a conflict framework
After reviewing overlap duration, number of conflicts, and sign-up behavior, I grouped scenarios into six warning states.
Clear schedule
No overlapping sessions detected. Tutors move through a standard sign-up flow without interruption.
Keep low-risk actions frictionless.
Light warning
Used when a tutor encounters a small overlap (typically under 20 minutes).
- 5-minute transition between sessions
- Small overlap with a recurring commitment
- Brief conflict with a one-time fill-in
Surface awareness while allowing tutors to proceed quickly.
Strong warning
Used when a conflict represents a meaningful scheduling risk.
- 20-minute overlap with a recurring session
- Significant conflict with an existing commitment
Require deliberate acknowledgement before proceeding.
Summary warning
Used when multiple conflicts exist simultaneously.
- Multiple recurring conflicts
- Multiple one-time conflicts
- Mixed recurring and one-time conflicts
Instead of presenting multiple individual alerts, the system summarizes all affected sessions in a single view.
Help tutors understand the total scheduling impact.
Batch warning
Used when tutors sign up for multiple sessions at once.
- Three fill-in sessions selected simultaneously
- Conflicts spread across several sessions or dates
The system identifies which selections create conflicts and summarizes risk across the batch.
Prevent accidental conflicts during bulk actions.
Selection error
Used when selected sessions conflict with each other — for example, a tutor selects two fill-ins that overlap in time.
Unlike other warnings, this scenario cannot be resolved through confirmation because both commitments cannot be fulfilled.
Prevent impossible scheduling combinations.
Defining overlap severity
To create consistent guidance across all scenarios, overlap duration became the primary indicator of risk.
Minor overlap
- Allow sign-up
- Display warning
- Require acknowledgment
Major overlap
- Escalate warning severity
- Flag for supervisor review
- Potentially restrict sign-up depending on policy
This framework ensured tutors received the appropriate level of guidance without introducing unnecessary friction.
Explorations
AI-assisted ideation
Before moving into wireframes, I used Google AI Studio as a collaborative ideation tool to rapidly explore multiple approaches for communicating scheduling conflicts. Rather than generating a single solution, I used AI to help expand the design space by exploring:
- Different warning and notification patterns
- Timeline-based conflict visualizations
- Ways to communicate overlap severity
- Confirmation and decision-making flows
Reviewing these explorations helped me quickly identify promising directions, compare trade-offs, and uncover edge cases that may have otherwise been overlooked.
Final Design
Outcome
Blocking behavior
Supporting informed decision-making
By replacing hard restrictions with contextual guidance, the design reduces accidental overlaps while preserving the flexibility tutors need to cover sessions in real-world situations.
Retrospect
Impact
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Lessons Learned
Share the key takeaways from this project.
With more time I’d like to
Describe future iterations and the research still needed.