Applied Purpose Science Research Internship 100% Remote

APPLIED PURPOSE SCIENCE RESEARCH LAB INTERNSHIP

Qualitative Study of How Students Use AI

Academic Credit Eligible

The Qualitative Research Internship offers undergraduate and graduate students a unique opportunity to explore how students are currently using artificial intelligence (AI) in their daily lives, learning, creativity, and decision-making—through the lens of purpose, identity, and human development.

Building on the Lab’s mission to study how people connect to purpose across life stages and contexts, this internship centers on a foundational research question:

How are students actually using AI today—and how does that use shape their sense of voice, agency, learning, and purpose?

Rather than focusing on technical development or coding, this internship emphasizes human-centered qualitative research, reflective inquiry, and ethical understanding. Students examine AI use as a lived experience, grounded in real stories, behaviors, and meaning-making processes.

Learning Goals

By the end of the internship, students will understand AI not as a technical system, but as a social and psychological tool shaping how students think, learn, reflect, and express themselves.

Participants will:

Develop foundational skills in qualitative research methods, including interviews, focus groups, reflective journaling, and thematic analysis

Examine how students use AI for learning, organization, creativity, emotional support, and reflection

Analyze how AI use intersects with purpose, identity, voice, and well-being

Practice ethical inquiry, cultural humility, and responsible research with human participants

Articulate their emerging identity as a purpose-driven researcher

Students will connect their own lived experiences with AI to broader social patterns, learning how personal narratives inform rigorous research questions.

Internship Structure (8–12 Weeks)

Phase 1: Purpose, Identity, and AI in Everyday Life

Students begin by reflecting on their own experiences with AI and reviewing existing research on student AI use. Emphasis is placed on positionality, ethics, and the role of purpose in inquiry.

Phase 2: Qualitative Data Collection

Students design and conduct qualitative research activities such as:

Semi-structured interviews with students

Focus groups or listening sessions

Reflective prompts and narrative data collection

They learn how to listen for meaning, not just usage patterns.

Phase 3: Analysis and Meaning-Making

Students engage in coding and thematic analysis, identifying patterns related to:

Agency and autonomy

Learning and motivation

Creativity and expression

Stress, overwhelm, and clarity

Purpose and future orientation

Phase 4: Purpose-Based Research Artifact

Each student produces a Qualitative Purpose Research Brief, which may include:

A thematic report

A narrative synthesis

A student-centered insight guide

A contribution to the Applied Purpose Science Research Library

The internship culminates in a Purpose Research Reflection, articulating how the student’s understanding of AI, research, and purpose has evolved.

Participation & Alignment

Students may participate for academic credit, volunteer service, or through Voice Up University, with all pathways receiving the same level of mentorship and access, consistent with the internship structure described in the provided template .

This internship aligns with:

Qualitative research competencies across social sciences

Ethical research standards involving human subjects

Emerging national conversations on AI, youth, and education

Purpose development and identity formation literature

It supports early pipeline development for future researchers, educators, policy analysts, designers, psychologists, sociologists, and human-centered technologists.

Precision. All the details, matter

Voice Up helps people move from vague ideas to clear insights. Language, lived experience, context, and nuance matter—because clarity is what transforms purpose into effective action.

Patience. We all need for gifts

Voice Up treats development as a process. Purpose is not rushed, forced, or performed. People need time, support, and practice to grow into their strengths and offer their gifts with confidence.

Empathy. We must understand your why

Voice Up centers the human story behind every goal. Understanding someone’s “why” is essential to building trust, belonging, and meaningful direction—especially across differences.

Purpose clarity and self-awareness (knowing what you are trying to solve and why) — aligning with the rising importance of motivation, self-awareness, and lifelong learning in employer forecasts.

Team collaboration and leadership (working across roles, managing ambiguity, building trust) — skills consistently named as growing in importance.

Applied research and evidence skills (qualitative and quantitative thinking, interpreting context, making responsible claims) — the backbone of “value creation” rather than “task completion,” and a throughline in 21st-century competencies.

Real deliverables for real communities (projects that leave the classroom and enter the world) — a direct response to an economy that increasingly rewards proof of capability, not just credentials.

Why the “tangible work product” requirement matters

In many traditional programs, a student’s best work disappears into a learning management system. In an AI economy, that invisibility becomes a liability: if anyone can generate a polished document, the differentiator becomes the ability to deliver something that is used, tested, and adopted by real people.

Voice Up’s hallmark requirement—students producing tangible work products designed for public benefit—functions like a form of skills “audit.” It pressures learning to become observable: Can a learner define a problem, engage stakeholders, iterate responsibly, and deliver something useful? Those are the kinds of outcomes that remain scarce even in a world of abundant automated content.

A new definition of career security

Career security used to mean mastering a stable body of knowledge. In the AI era, security increasingly means mastering a stable set of human capabilities:

the ability to learn continuously

the ability to work with and through other people

the ability to exercise judgment

the ability to translate insight into action

the ability to measure what changed and why

Those aren’t just “soft skills.” They are the operating system of modern work.

The bigger significance: building a bridge between education, science, and the labor market