Research Problem

The Challenge

• Workplace Transformation: 40% of US workers are in hybrid/remote settings (WFH Research, 2025)
• Mid-Career Gap: Professionals with 5-20 years experience lack tailored development opportunities
• AI Integration: HR leaders struggle with "developing competencies and managing the coexistence of humans and machines" (Bhatt & Muduli, 2022, p. 678)
• Distributed Workforce Needs: Limited in-person interactions increase demand for effective professional development

Research Questions

Theoretical Framework

Application: Coaching practices supporting these needs promote intrinsic motivation, personal growth, and sustained behavioral change.

Literature Review: Three Key Themes

1. Complementary Strengths of AI and Human Coaching

• AI Advantages: 24/7 access, consistent feedback, scalable personalization (Terblanche et al., 2022)
• Human Advantages: Empathy, trust, transparency, emotional intelligence (Passmore et al., 2025)
• Mixed Results: Hybrid models sometimes show lower retention rates (Loughnane et al., 2025)

2. Strengths-Based Interventions Support SDT

• Promotes autonomy by empowering individuals to "claim" unique talents
• Builds competence through mastery and performance improvements
• Enhances relatedness through improved teamwork understanding

3. Critical "Human Elements"

• Empathy: Authentic understanding and validation
• Trust: Functional (AI) vs. Relational (Human)
• Collaboration: Working alliance as prerequisite for effectiveness

Research Design

Convergent Mixed-Methods (Qualitative Primary)

• Primary: Thematic analysis with Interpretive Phenomenological Analysis (IPA)
• Secondary: Working Alliance Inventory-Short Revised (WAI-SR) for coaching
• Duration: 6 weeks per participant, 4 hours total commitment

Participants

• N = 24 mid-career professionals (5-20 years experience)
• Criteria: US residents, full-time employed, remote ≥50% of week
• Groups: 8 participants each in Human-led, AI-only, AI-augmented

Intervention

• Framework: Standardized CliftonStrengths coaching
• Format: Two 45-minute sessions over 3-4 weeks

Methods & Rationale

Rationale for Design Choices

• IPA: Ideal for exploring subjective experiences of psychological need fulfillment
• CliftonStrengths: Validated framework that naturally supports all three SDT needs
• Mixed-Methods: Comprehensive understanding of both experience and measurable outcomes
• Three Modalities: Allows direct comparison of human, AI, and hybrid approaches

Data Collection Process

Three Phases

• Phase 1 (1 hour): Pre-assessment, CliftonStrengths assessment, availability confirmation
• Phase 2 (2 hours total): Two coaching sessions with post-session WAI-SR and brief interviews
• Phase 3 (1 hour): In-depth semi-structured interview within one week

Data Sources

• Observational: Transcribed coaching sessions across all modalities
• Survey: WAI-SR data for quantitative triangulation
• Interview: Rich qualitative data on lived experiences
• Field Notes: Real-time observations of participant dynamics

Triangulation Strategy

Multiple data sources to validate findings and provide comprehensive understanding of coaching experiences across modalities (Creswell & Poth, 2018).

Initial (Anticipated) Findings

Modality-Specific Expectations

• Human-Led: Strongest relatedness support through authentic connection
• AI-Only: Competence advantages via consistent, data-driven feedback
• AI-Augmented: Potential "best of both worlds" or complexity challenges

Conceptual Framework Validation

• "Human Elements": Empathy, trust, and collaboration as core to coaching
• Trust Types: Relational (human) vs. Functional (AI) trust distinction
• Hybrid Complexity: Challenges in navigating dual modalities simultaneously

Anticipated Challenges

Data Collection Challenges

• Technology Comfort: Varying participant comfort levels with AI
• Recruitment: Finding participants willing to engage in self-directed AI coaching
• Novelty Bias: AI coaching newness may influence perceptions
• Remote Dynamics: Capturing nuanced interactions in virtual settings

Analytical Challenges

• Attribution: Distinguishing modality effects from individual traits
• Personality Variables: Accounting for different AI comfort levels
• Hybrid Complexity: Parsing multi-party interaction dynamics
• Bias Management: Researcher expectations influencing interpretation

Mitigation Strategies

• Careful recruitment screening and orientation
• Multiple data sources for triangulation
• Reflexive journaling throughout analysis
• Peer debriefing for validation

Research Significance & Next Steps

Expected Contributions

• Theoretical: Extend SDT application to AI-enabled coaching contexts
• Practical: Evidence-based guidance for AI coaching tool design
• Methodological: Framework for evaluating hybrid human-AI interventions
• Professional: Inform distributed workforce development strategies

Implications for Practice

• Design principles for preserving "human elements" in AI coaching
• Guidelines for when to use human, AI, or hybrid approaches
• Understanding of mid-career professional development needs
• Insights for remote work professional development