AI and ePortfolios

Intelligent Portfolios: How AI Agents Can Scaffold Learner ePortfolios and Teaching Portfolios in Higher Education

In the evolving landscape of higher education, portfolios—both ePortfolios for learners and teaching portfolios for educators—have become essential tools for documenting growth, reflecting on practice, and demonstrating competence. As these portfolios increasingly serve as dynamic, digital artifacts used for academic, professional, and institutional purposes, the integration of AI agents presents a transformative opportunity. Intelligent agents, capable of natural language processing, content generation, and semantic organization, can guide students and faculty in creating, scaffolding, and curating portfolios that are both reflective and rigorous.

ePortfolios: A Constructivist Platform for Learner Growth

ePortfolios are grounded in constructivist and reflective pedagogies, offering students a platform to synthesize learning, develop self-regulation, and showcase competencies (Barrett, 2010). Yet, without structured guidance, many learners struggle to articulate connections between artifacts, reflect meaningfully, or align their ePortfolio with academic and professional standards (Ring et al., 2017).

Scaffolding ePortfolio Development with AI Agents

AI agents can address this gap by scaffolding the ePortfolio design process through tailored prompts, intelligent templates, and iterative feedback. For instance, an AI assistant embedded within an ePortfolio platform could prompt learners to:

• Align artifacts with specific learning outcomes or competency frameworks.

• Reflect on the evolution of their thinking over time.

• Reframe informal learning experiences (e.g., internships, volunteer work) into academically relevant narratives.

These supports align with Zimmerman’s (2002) model of self-regulated learning, as AI agents facilitate planning (by recommending portfolio structures), monitoring (through reflection prompts), and evaluation (by identifying strengths and gaps across submitted artifacts).

Organization and Semantic Tagging

AI agents can also use semantic analysis to tag and categorize portfolio content automatically. For example, a student submitting a final project could receive a suggestion: “This project demonstrates skills in data analysis and ethical reasoning. Would you like to tag it accordingly and link it to your critical thinking learning outcome?” This level of support enhances coherence and alignment across the portfolio (Batson, 2018).

Teaching Portfolios: Supporting Faculty Reflection and Advancement

In faculty development and academic career advancement, teaching portfolios have long served as a vehicle to present one's instructional philosophy, course innovations, assessment practices, and student feedback (Seldin et al., 2010). More recently, institutions have encouraged integration of research agendas and teaching statements, requiring faculty to demonstrate a broader commitment to scholarship and equity.

AI-Assisted Drafting and Revision

An AI agent can help faculty create first drafts of critical portfolio elements—such as a teaching philosophy statement—by analyzing instructional artifacts, student evaluations, and course materials. Based on this input, the agent could produce a reflective narrative grounded in pedagogical theory, e.g.,:

“Your syllabi emphasize formative assessment and active learning. Would you like to include a paragraph about how these reflect your constructivist teaching philosophy?”

Similarly, the AI can assist in formulating a research agenda by analyzing recent publications, projects, and institutional strategic priorities to ensure alignment and clarity (Luo et al., 2023). For early-career faculty, such guidance can reduce cognitive load and provide models for genre-specific writing conventions.

Organizing and Versioning Portfolios

In both teaching and ePortfolios, the ability to organize content over time is critical. AI agents can assist by:

• Suggesting chronological or thematic structures.

• Tracking changes to teaching practices or research goals over semesters.

• Generating summaries of growth or transformation over time.

These features are especially valuable in promotion and tenure reviews, where faculty must present a longitudinal narrative of impact (Hutchings et al., 2011).

Dialogic Feedback and Coaching

One of the most promising affordances of AI agents is their capacity to simulate dialogic coaching. For instance, an agent could ask:

“You’ve listed course redesigns as evidence of innovation. Can you describe how student feedback informed your redesign choices?”

Such prompts not only guide the content but cultivate reflective metacognition—a key feature of effective portfolios (Rodgers, 2002). Moreover, AI agents can help users anticipate reviewer expectations by offering checklists aligned with institutional or disciplinary standards.

Ethical Considerations

As with all educational applications of AI, ethical design is paramount. Portfolios often include sensitive reflections, personal narratives, and proprietary work. Developers must ensure that AI agents operate under strict data privacy protocols, offer transparent models, and avoid template-driven homogeneity that erodes authentic voice (Holmes et al., 2022). Faculty and students should be co-designers in determining what the AI supports and how it provides feedback.

AI agents represent a new generation of portfolio support tools—intelligent, interactive, and learner-centered. Whether scaffolding student ePortfolios to capture integrated learning or supporting faculty in constructing reflective, evidence-based teaching portfolios, AI offers personalized guidance that enhances reflection, organization, and coherence. When ethically implemented, these agents can democratize access to high-quality academic documentation and foster deeper engagement with the processes of teaching, learning, and scholarship.

References

Barrett, H. (2010). Balancing the two faces of ePortfolios. Education Canada, 50(2), 31–35. https://www.edcan.ca/articles/balancing-the-two-faces-of-eportfolios 

Batson, T. (2018). The ePortfolio idea: Documenting and reflecting on learning. In D. Cambridge (Ed.), ePortfolios in higher education: A decade of development (pp. 1–17). Stylus.

Holmes, W., Bialik, M., & Fadel, C. (2022). Artificial intelligence in education: Promises and implications for teaching and learning (2nd ed.). Center for Curriculum Redesign.

Hutchings, P., Huber, M. T., & Ciccone, A. (2011). The scholarship of teaching and learning reconsidered: Institutional integration and impact. Jossey-Bass.

Luo, L., Freeman, M., & Zhang, Y. (2023). The role of generative AI in academic portfolio development: A qualitative exploration. Innovations in Education and Teaching International, 60(3), 278–289. https://doi.org/10.1080/14703297.2023.2187482 

Ring, G., Waugaman, C., & Brackett, B. (2017). The value of ePortfolios in a graduate teacher education program. International Journal of ePortfolio, 7(1), 13–22. https://www.theijep.com/pdf/IJEP233.pdf 

Rodgers, C. (2002). Defining reflection: Another look at John Dewey and reflective thinking. Teachers College Record, 104(4), 842–866. https://doi.org/10.1111/1467-9620.00181 

Seldin, P., Miller, J. E., & Seldin, C. A. (2010). The teaching portfolio: A practical guide to improved performance and promotion/tenure decisions (4th ed.). Jossey-Bass.

Zimmerman, B. J. (2002). Becoming a self-regulated learner: An overview. Theory Into Practice, 41(2), 64–70. https://doi.org/10.1207/s15430421tip4102_2