#Case Study

Soft Skills Training Case Study with 80 programmers

This report summarizes the engagement, retention, and completion outcomes of the programmers, along with the main takeaways.

Learning Path Progress
297%
Assigned path completions vs. baseline expectation
Training Volume
~400 h
Total minutes ≈ 24,130 across program
Daily Activity
~47%
Daily active users on a typical workday
Retention at 3rd month
83%
Users with any activity in final month

Background & Problem

The company’s tech team was looking for a way to improve how they worked together without disrupting sprints or adding recurring workshops. They faced delays in alignment, stalled pull requests, unpredictable sprint outcomes, and low engagement with past training efforts.

Team Setup

  • 80 programmers
  • Cross-functional dependencies

Main Challenge

  • Limited meeting time
  • Frequent context switching
  • Difficulty sustaining skills training during active sprints

Objective

  • Short, frequent practice
  • Build lasting habits of engagement
  • Measurable progress

Engagement Challenge

Traditional soft skills workshops had been used in the past, but they were difficult to sustain due to limited engagement and a lack of consistent practice. To address this, WiseWorld introduced daily micro-practice, creating a more continuous and effective learning experience.

Traditional Workshops

• Usually limited to 1–2 intense days of activity
• Skills quickly forgotten
• No measurable outcomes
• Low interest in future sessions

Daily Micro-Practice

✓ 5 minutes integrated into each workday
✓ Steady participation sustained for 3 months
✓ Skills reinforced regularly as part of routine

scene_on_wiseworld

WiseWorld Method

Three-month program using weekday micro-practice (~5 minutes per day). Activities targeted 44 soft-skill dimensions in 6 main categories including problem solving, leadership, cognitive abilities, work ethics and communication.

Design

  • Frequency: Weekdays and self-paced
  • Modality: AI generated real-life episodes
  • Personalization: Learning paths generated based on each participant's goals.

Measurement

  • Activity: Dialogues completed each day.
  • Retention: Any activity during final month.
  • Progress: Episodes and path completions recorded by platform.
learning_path_on_wiseworld

Learning Goals

We grouped the learning goals into three major categories. Here’s the breakdown of the 3 most common goals and their percentages:

1. Career & Work Growth

Level Up at Work

  • Percentage: ~50%
  • Includes: Level Up at Work, improving leadership, handling tricky situations, speaking up, visionary thinking, career development, programming skills, managing stress/chaos, etc.

2. Personal Life Improvement

Upgrade My Personal Life

  • Percentage: ~32%
  • Includes: Upgrade My Personal Life, feeling calmer, more confident, more energy, better focus, managing personal/work balance, overcoming fear, improving self-confidence, time management, etc.

3. Connection & Communication

Grow My Connections

  • Percentage: ~18%
  • Includes: Grow My Connections, improving social skills, meeting new people, deeper conversations, handling tough conversations, shyness, building teamwork/communication skills.
target_skills_on_wiseworld

Target skills

These are the 6 most commonly selected target skills out of the 44 available skills during learning path generation:

1. Active Listening

Communication category

2. Public Interaction

Communication category

3. Confidence

Leadership category

4. Decision Making

Problem Solving category

5. Time Management

Work Ethic category

6. Resilience

Work Ethic category

learning_hint_on_wiseworld

AI Soft Skills Coach

During the episodes, participants had the option to ask for a hint from Wise, the AI Soft Skills coach.

2,396

Real-time AI learning hints

Average Team PowerWheel

Strengths and Gaps In the Team

  • ✓ It replaced vague feedback with concrete, visual data.
  • ✓ Team members could see their own growth over 44 soft skills.
  • ✓ The team gained a real-time, unbiased view of soft-skills development.
corp_pilot_7_pw

Results

Engagement and continuity were sustained without affecting their daily tasks.

Daily Activity

47%

Share of participants active on a typical weekday.

Weekly Momentum

5.2 episodes

Average progress per user per week.

Retention

83%

Users with any activity in the final month.

Training Volume

~400 hours

≈24,130 minutes total across 80 participants.

Learning Path Completion

297%

Completions relative to baseline expectation.

Trained Skills

~15 skills

Number of trained skills per user.

Programmers Stayed Engaged When:
  • Practice was short and consistent. Just 5 minutes a day
  • Learning paths matched their goals and interests
  • Progress felt clear and rewarding along the way