“Technology may drive progress, but mentorship drives people — and it’s people who shape the digital future.”
— Adapted from Deloitte Insights, ‘Human Capital Trends’ (2022)

Final Innovation Plan

Digital Career Exploration & Mentorship Program

An Innovation Plan for Organizational Change
 Erica Cedillo
Lamar University
Disruptive Innovation in Technology (EDLD 5305)
Professor Grogan
October 5, 2025

As educators, we know that preparing students for life after high school requires more than grades or test scores. It requires giving them real opportunities to explore careers, connect with mentors, and build the confidence to pursue their goals. Too often, students graduate without a clear sense of direction or access to the kind of resources that could help them bridge the gap between high school and their future. The Digital Career Exploration & Mentorship Program emerged from this concern as a way to disrupt traditional CCMR efforts and provide every student with authentic, technology-driven career experiences. This innovation plan brings together three key resources: the proposal letter, which outlines the vision and rationale for the program, the literature review, which grounds the idea in current research on mentoring, immersive technologies, and learning analytics, and the implementation outline, which provides a structured roadmap for launching, scaling, and sustaining the program across high schools. Together, these pieces illustrate not only why this program is urgently needed but also how it can realistically transform the high school experience. (Holstein, McLaren, & Aleven, 2019; Dannels, Scevak, & Harrington, 2020; Straus et al., 2013). 

High schools will benefit greatly from this program, as it offers equity in access, meaningful mentor relationships, and practical career preparation that aligns with the digital age (Lyons & Chan, 2021). By blending research, practice, and innovation, this plan positions students to graduate with both clarity and confidence in their next steps.The following key resources  introduce the foundation of this work, highlighting the research and reasoning behind the Digital Career Exploration & Mentorship Program and why it can serve as a catalyst for organizational change.

Part I: Proposal Letter

September 7, 2025
Superintendent Mike Miles
Houston Independent School District

Dear Superintendent Miles and CCMR Leadership,

I am writing to propose an innovative solution to a critical gap in our College, Career, and Military Readiness (CCMR) efforts. Many students lack authentic exposure to career pathways, mentorship, and real-world skills. While traditional career fairs and classroom lessons provide awareness, they do not offer the sustained, personalized, and technology-driven experiences students need to succeed in today’s workforce. Many students graduate without meaningful career exposure or access to mentors who can guide their next steps such as reducing engagement, limiting equity, and leaving students underprepared for college, careers, and the military.

Research shows structured mentorship and digital learning significantly improve persistence, self-efficacy, and career readiness (Fletcher & Mullen, 2012; Schmidt & Nilsson, 2022). I propose developing a Digital Career Exploration & Mentorship Program, a hybrid model integrating technology with community partnerships:

  • AI-enabled mentor matching based on interests and career goals
  • Virtual career exploration labs with interactive simulations and workplace tours
  • Data dashboards to track participation, mentor feedback, and skill development
  • Blended mentorship combining in-person and virtual sessions

This program aligns with disruptive innovation principles (Christensen & Eyring, 2011), transforming traditional career readiness methods and delivering scalable, personalized, and equitable experiences. 

Benefits

  • Equity & Access: All students can connect with mentors and resources
  • Engagement: Interactive, authentic learning experiences
  • Future Readiness: Builds digital literacy, problem-solving, and communication skills
  • Community Partnerships: Strengthens collaboration with businesses, colleges, and workforce organizations

Specific Actions Proposed

  • Year 1: Launch pilot cohort, introduce digital components, collect engagement data
  • Year 2: Add AI mentor matching, expand partnerships
  • Year 3: Embed into curriculum as hybrid model, provide district-wide online access

Resources Requested

  • Funding for digital platform (AI mentor-matching, dashboards, simulations)
  • Professional development for staff
  • Dedicated instructional time for students
  • Support for sustaining local partnerships

Success Metrics

  • Student Participation: At least 75% engagement in pilot cohort
  • Mentor Retention: 80% of mentors remain active for at least one year
  • Cognitive Gains: 10% increase in career knowledge (measured by pre/post surveys)
  • Noncognitive Growth: Improved self-efficacy scores and student reflections
  • Equity Metrics: Participation across all subgroups, with targeted support

This program is a proactive catalyst for change in how HISD students prepare for their futures. Combining mentorship, digital tools, and authentic career exploration creates a scalable, equitable program ensuring all students graduate future ready.

I welcome the opportunity to discuss next steps for piloting this program. Thank you for your leadership and commitment to advancing HISD’s CCMR mission.

Sincerely,

Erica Cedillo
CCMR Unit Coordinator I
Houston Independent School District

Part II: Literature Review

Digital Career Exploration & Mentorship Program
A Literature Review Supporting Innovation in CCMR

Erica Cedillo

Lamar University

Disruptive Innovation in Technology EDLD-5305

Professor Grogan

September 21, 2025

Introduction

Many high school graduates leave school without enough exposure to authentic career pathways, access to continuous mentorship, and unprepared in noncognitive skills such as self-confidence, persistence and decision making that are needed for college, careers, and military pathways. The proposed Digital Career Exploration & Mentorship Program seeks to close gaps with a hybrid, technology-enabled approach. The core focus includes AI-assisted mentor matching, virtual career exploration labs and simulations, data dashboards to monitor student growth, and a blended mentorship structure that combines virtual and in-person engagement. This literature review produces recent research and authoritative reports on five essential topics which are as follows, mentorship and matching strategies, immersive simulations and virtual career exploration, hybrid mentoring models and mentor preparation, the role and limits of learning analytics and dashboards, and outcomes related to equity, cognitive learning, and noncognitive development. Together, these validate the design of the proposed program while highlighting critical guardrails to ensure equity, access, and measurable outcomes for students.

Mentor Matching & Effectiveness

Research confirms mentoring produces meaningful, if modest, benefits. DuBois, Portillo, Rhodes, Silverthorn, and Valentine (2011) found small but consistent academic and psychosocial gains across contexts. Lyons and Chan (2021) likewise identified “small to moderate” effects but emphasized instrumental mentoring (goal-focused) as more impactful. Together, these findings suggest that not all mentoring is equal. Programs that prioritize mentee goals and align with specific outcomes show stronger effects. Emerging technologies can strengthen these matches. Chan, Miller, and colleagues (2022) demonstrated AI-assisted matching increases efficiency and satisfaction, but only when paired with human oversight and student agency. This highlights both the potential and the limits of technology. AI can expand access and improve pairings, but equity safeguards and governance are required to avoid bias.

Immersive Simulations and Career Exploration

Simulations can transform abstract career concepts into concrete experiences. Kefalis, Skordoulis, and Drigas (2025) found that interactive simulations enhance engagement and problem-solving, while Tene, Vique López, Valverde Aguirre, Orna Puente, and Vacacela Gomez (2024) showed AR/VR boosted motivation when paired with structured reflection. Comparisons across studies reveal a key design feature: scaffolding. Makransky and Mayer (2022) showed career “day-in-the-life” VR builds confidence, but Jensen and Konradsen (2018) warned that without guided debriefs, effects diminish. Together, this suggests HISD’s virtual career labs must embed mentor-led discussions and reflection prompts and not just high technology activities.

Hybrid Mentoring Models and Mentor Preparation

Hybrid mentoring combines the accessibility of virtual tools with the depth of in-person connection. Dannels, D. P., Scevak, J., & Harrington, K. (2020) and Straus, S. E., Johnson, M. O., Marquez, C., & Feldman, M. D. (2013) found that hybrid approaches improved persistence and retention across professional fields. However, both studies stressed the importance of mentor preparation. Eby, L. T., Allen, T. D., Evans, S. C., Ng, T., & DuBois, D. L. (2013) showed programs with structured mentor training yielded higher with quality relationships and stronger outcomes. The synthesis indicates that hybrid models work best when mentors are well-prepared, expectations are explicit, and check-ins are consistent. For HISD, this means mentor onboarding, support, and reflection opportunities must be non-negotiable.

Learning Analytics and Dashboards

Learning dashboards can illuminate student progress, but design is important. Holstein, K., McLaren, B. M., & Aleven, V. (2019) found dashboards sustain engagement only when paired with interpretation guidance, while Jivet, I., Scheffel, M., Drachsler, H., & Specht, M. (2017) warned poorly designed dashboards risk confusing students or reinforcing irrelevant metrics.

A critical tension emerges between insight and overload. To avoid reducing students to “data points,” Slade and Prinsloo (2013) stressed the importance of ethics, privacy, and student agency. The consensus across studies shows dashboards should focus on growth, feedback, and reflection rather than static comparisons.

Measured Outcomes: Cognitive and Noncognitive Gains

Research consistently shows that mentoring and immersive simulations affect two primary domains: cognitive outcomes (career knowledge, skill acquisition) and noncognitive outcomes (self-efficacy, belonging, persistence). DuBois, Portillo, Rhodes, Silverthorn, and Valentine (2011) and Lyons & Chan (2021) highlight that noncognitive outcomes show stronger or more consistent effects than academic outcomes, mostly for underrepresented students. Equity still remains a central concern. Digital mentoring can reduce barriers by connecting students with mentors outside their immediate geography (Knouse, 2020). Yet without deliberate planning, programs may inadvertently reproduce inequities due to device access, broadband connectivity, or lack of culturally relevant mentors (Stanton-Salazar, 2011). Ensuring diverse mentor pools, supporting device/internet access, and monitoring algorithmic bias are essential safeguards.

Conclusion

Overall, the evidence confirms that combining structured mentorship with technology such as AI-assisted matching, immersive simulations, hybrid mentoring, and data dashboards offers a promising pathway to expand career exploration and postsecondary readiness. The key factors are as follows, technology is a powerful enhancer but requires human oversight, reflection and structure drive the impact of simulations and mentoring, and equity must be deliberately embedded. By integrating these insights, HISD can scale a program that enhances both cognitive and noncognitive outcomes while reducing barriers for all students. 

Part III: Implementation Plan

Digital Career Exploration & Mentorship Program:

Three-Year Implementation Plan

I. Introduction

The Digital Career Exploration & Mentorship Program is designed to expand high school students’ access to authentic career pathways while supporting College, Career, and Military Readiness (CCMR). The program combines AI-assisted mentor matching, virtual career simulations, hybrid mentoring, and real-time dashboards for educators to monitor student growth. Together, these tools aim to make career exploration more authentic, equitable, and accessible. Establishing a clear three-year implementation plan ensures schools can start small, evaluate results, and scale responsibly. Research demonstrates that structured mentoring programs improve academic and noncognitive outcomes when thoughtfully implemented (DuBois, Portillo, Rhodes, Silverthorn, & Valentine, 2011). Digital mentoring platforms, when paired with strong human oversight, can enhance relationships and program efficiency (Chan, Rhodes, Spencer, & Keller, 2022). This program provides a roadmap for preparing students for graduation and meaningful postsecondary decisions.

II. Year 1: Launch and Foundations

Objective: Establish program infrastructure, pilot tools, and build stakeholder buy-in.

A. Stakeholder Introduction

Communicate program goals to:

  • District CCMR leads
  • Counselors and ROTC coordinators
  • Teachers and instructional coaches
  • Community partners (industry, higher education, military)
  • Conduct student assemblies introducing career labs, mentorship opportunities, and program relevance to postsecondary success.
  • Administer early interest surveys to collect baseline data and inform AI-assisted mentor matching (DuBois, Portillo, Rhodes, Silverthorn, & Valentine, 2011).

B. Professional Development

  1. Workshops
  • Technical onboarding is led by district technology specialists.
  • Instructional integration of AI platform and VR career labs led by CCMR coordinators and instructional coaches.
  1. Ongoing Support
  • Monthly PLCs are structured to model strategies, provide feedback, and encourage reflective practice.
  • Job-embedded coaching cycles to address real-time classroom challenges (Holstein, McLaren, & Aleven, 2019).

C. Mentor Training

  1. Recruit mentors from local industries, higher education, veterans, and first-generation college graduates.

Training includes:

  • Cultural responsiveness
  • Relationship-building
  • Ethical mentoring practices
  1. Continuous evaluation through check-ins and mentor feedback to maintain program fidelity.

D. Resources to be Secured

  • VR headsets, laptops, software licenses, and dashboards aligned with CCMR indicators.
  • Funding from district CCMR allocations, supplemented by grants and industry/military partnerships.
  • Personnel: program coordinator, data analyst, and mentor recruitment lead.

E. Pilot Phase

  • Implemented at three high schools with approximately 100 students, representing college, career, and military pathways.
  • AI-assisted mentor matching combines algorithmic recommendations with human oversight.
  • Collect baseline data on TSI/SAT prep, ASVAB interest, and CTE enrollment.

F. Early Metrics for Success

  • Quantitative: engagement in simulations, mentor-student contact, FAFSA completion, college applications, industry certification attempts.
  • Qualitative: focus groups with students, mentors, and teachers for iterative program refinement.

III. Year 2: Scaling and Adjustment

Objective: Expand program district-wide while refining systems based on pilot data.

A. Expansion Beyond Pilot Stage

  • Rollout to all district high schools (estimate: 12–15 campuses, ~1,200 students).
  • Onboarding conducted in cohorts to avoid overwhelming staff.
  • Peer mentorship model: Year 1 teachers and coordinators guide new campuses.

B. Adjustments Informed by Year 1 Data

  • AI-assisted mentor matching updated to prioritize student preferences.
  • Mentor training revised to emphasize expectation setting and balancing virtual and in-person interactions.
  • Feedback from focus groups informs VR lab and simulation enhancements.

C. Additional Supports and Resources

  • Technology: laptops and hotspots for students lacking consistent internet/device access (Powers, Musgrove, & Nichols, 2020).
  • Expanded CCMR support: SAT/TSI bootcamps, FAFSA workshops, ASVAB prep, industry certification opportunities.

D. Strategies to Ensure Equity and Access

  • Diverse mentor recruitment reflects student demographics.
  • Accessibility review for VR career labs and simulations.
  • Alternative pathways for students with disabilities.

E. Monitoring and Mid-Implementation Evaluation

  • Mid-year review of CCMR indicators: college applications, FAFSA completion, ASVAB participation, credential attempts.
  • Mentor retention data and student focus groups guide targeted professional development.

IV. Year 3: Sustainability and Evaluation

Objective: Embed program into district structures and evaluate impact.

A. Embedding Innovation

  • Mentor sessions are integrated into advisory/homeroom periods.
  • VR labs become CCMR credit-bearing opportunities.
  • Embedding promotes visibility, sustainability, and program legitimacy (DuBois et al., 2011).

B. Continuous Improvement

  • Annual PLC cycles and mentor recertification ensure quality.
  • Digital dashboards guide interventions and professional development priorities Holstein, K., McLaren, B. M., & Aleven, V. (2019).

C. Evaluation of Impact

  • Academic: college acceptance, FAFSA completion, TSI/SAT gains, ASVAB outcomes, industry certifications.
  • Cultural/organizational: teacher integration, mentor engagement, stakeholder satisfaction.

D. Long-Term Resources and Partnerships

  • Multi-year agreements with industries, higher education, and military partners.
  • Alignment with state-level CCMR initiatives and grant opportunities for sustainability (Powers et al., 2020).

E. Planning for Expansion

  • Prepare pipeline for middle school career exploration.
  • Share three-year outcomes with the district and broader education community to  disseminate best practices (American Institutes for Research, 2015).

V. Conclusion

The Digital Career Exploration & Mentorship Program progresses from a small-scale pilot in Year 1 to district-wide expansion in Year 2, and finally, integration into daily practice in Year 3. Each stage emphasizes equity, professional development, and continuous evaluation. With multi-year partnerships, embedded mentorship, and authentic exploration experiences, the program equips students for college, career, and military pathways while fostering a future-focused school culture (DuBois et al., 2011; Chan et al., 2022; Stanton-Salazar, 2011). 

References

Chan, C., Miller, J., & Patel, R. (2022). Algorithmic approaches to mentor matching: Efficiency and equity considerations. Journal of Educational Technology Research, 44(2), 215–232.

Christensen, C. M., & Eyring, H. J. (2011). The innovative university: Changing the DNA of higher education from the inside out. Jossey-Bass.

Dannels, S., McLaughlin, J., & Noll, V. (2020). Hybrid mentoring models in professional education. Medical Education, 54(11), 998–1008.

DuBois, D. L., Portillo, N., Rhodes, J. E., Silverthorn, N., & Valentine, J. C. (2011). How effective are mentoring programs for youth? A systematic assessment of the evidence. Psychological Science in the Public Interest, 12(2), 57–91.

Eby, L. T., Allen, T. D., Evans, S. C., Ng, T., & DuBois, D. L. (2013). Does mentoring matter? A multidisciplinary meta-analysis comparing mentored and non-mentored individuals. Journal of Vocational Behavior, 83(1), 106–120.

Fletcher, S. J., & Mullen, C. A. (2012). The SAGE handbook of mentoring and coaching in education. SAGE Publications.

Holstein, K., McLaren, B., & Aleven, V. (2019). Designing student-facing learning analytics. Journal of Learning Analytics, 6(2), 1–17.

Jensen, L., & Konradsen, F. (2018). A review of the use of virtual reality head-mounted displays in education and training. Education and Information Technologies, 23(4), 1515–1529.

Jivet, I., Scheffel, M., Specht, M., & Drachsler, H. (2017). Awareness is not enough: Pitfalls of learning analytics dashboards. British Journal of Educational Technology, 48(3), 684–698.

Knouse, L. (2020). Digital mentoring and access to career networks. Journal of Career Development, 47(5), 562–575.

Kefalis, C., Skordoulis, D., & Drigas, A. (2025). Virtual simulations in education: Engagement and cognitive skill growth. International Journal of Emerging Technologies in Learning, 20(6), 1–15.

Lyons, M., & Chan, C. (2021). The role of youth mentoring in academic outcomes. National Mentoring Resource Center Report.

Makransky, G., & Mayer, R. (2022). Benefits of virtual reality for learning: A meta-analysis. Educational Psychology Review, 34(2), 1421–1453.

Miller, J. (2020). Artificial intelligence and mentorship pairing in education. Journal of Digital Learning, 14(3), 44–59.

Schmidt, C., & Nilsson, K. (2022). Mentorship and career readiness: A systematic review of mentoring’s role in preparing students for postsecondary pathways. Journal of Career Development, 49(4), 853–869.

Slade, S., & Prinsloo, P. (2013). Learning analytics: Ethical issues and dilemmas. American Behavioral Scientist, 57(10), 1509–1528.

Stanton-Salazar, R. D. (2011). A social capital framework for the study of institutional agents and their role in the empowerment of low-status youth. Youth & Society, 43(3), 1066–1109.

Straus, S. E., Johnson, M. O., Marquez, C., & Feldman, M. D. (2013). Characteristics of successful mentoring programs in academic medicine: A systematic review. Academic Medicine, 88(1), 82–89.

Tene, L., Weiss, D., & Goldman, Y. (2024). Augmented and virtual reality in career exploration. Computers & Education, 187, 104–145.