Purpose
Build practical familiarity with AI concepts relevant to biology while practicing careful technical writing, source citation, and clear communication for a professional portfolio.
Program Overview
This page documents the public structure of an unpaid educational research internship hosted by praxagent. The primary goal is learning: guided reading, research exposure, responsible AI use, technical communication, and a credible public body of work.
This internship is filled by Huriah Fitzgerald, Class of 2026, University of Delaware. Portfolio work is in progress; reviewed projects will be added here as they are completed.
Mentorship
The internship is structured around independent learning with weekly remote mentorship discussions, draft review, research guidance, and feedback on clarity, accuracy, limitations, and presentation.
Boundaries
This is an unpaid educational mentorship, not employment or paid contractor work. Projects use public resources and focus on the intern’s learning and portfolio. The content boundaries below apply to every project.
AI in Biological Science & Education Consultant
Jennifer Hoffman advises on the program’s scientific direction and reviews biology content for accuracy, drawing on her experience in plant biology, teaching, and software development.
Jennifer Hoffman
AI in Biological Science & Education Consultant
Jennifer holds a Master of Science in Biology from Syracuse University, with a focus on plant developmental biology. Her background includes teaching Earth Sciences and professional software development at Gateway Ticketing Systems. She guides the program’s scientific direction, reviews biology content, and helps connect AI methods to biological research and education.
Learning Objectives
The program emphasizes useful foundations for AI-assisted biology research while keeping uncertainty, limitations, and careful public research writing front and center.
AI Foundations
- Language models, embeddings, retrieval-augmented generation, and model evaluation.
- Data quality, limitations, hallucination risk, and responsible use of AI assistance.
Biology Context
- Introductory exposure to bioinformatics, genomics, protein modeling, and biomedical retrieval.
- Reading, summarizing, and critiquing public papers and technical resources.
Technical Communication
- Accessible explainers, literature notes, diagrams, notebooks, and project summaries.
- Careful sourcing, limitation statements, and professional portfolio presentation.
Planned Portfolio Work
These are learning targets. The final set may change as interests, available time, and mentor feedback shape the research path.
Technical Posts
Three to five public technical posts, literature reviews, or research notes explaining selected AI + biology topics for a professional audience.
Demos or Diagrams
One or two lightweight educational demos, notebooks, diagrams, or prototype artifacts using public resources, toy examples, or synthetic data where feasible.
Final Summary
A final public summary describing topics studied, skills developed, artifacts completed, lessons learned, and possible next steps.
Authorship: Portfolio work is intended to be credited to the participating
intern. praxagent may host, format, archive, and link to the work to document the educational
internship and support professional development.
Initial Learning Plan
The plan is intentionally flexible. It can be adjusted as interests, skills, and project ideas develop.
Phase 1
Orientation
Set expectations for citations, responsible AI use, public writing, and safe handling of data.
Phase 2
Reading and Topic Selection
Read introductory resources or papers related to AI for biology, bioinformatics, protein models, genomics, biomedical retrieval, or scientific literature analysis.
Phase 3
First Research Note
Draft a technical explainer or literature-review research note, receive feedback, revise, and post it publicly.
Phase 4
Educational Demo
Explore a small notebook, diagram, or prototype using public resources, toy data, or synthetic/example data.
Phase 5
Portfolio Development
Draft and post additional research artifacts while improving clarity, citations, diagrams, limitation statements, and presentation.
Phase 6
Final Summary
Prepare a portfolio summary, resume description, professional profile language, and optional reflection on lessons learned.
Example Project Topics
Potential topics include literature review workflows, biological knowledge retrieval, model limitations, and responsible communication of AI-assisted research.
How language models can assist literature review in biology.
Retrieval-augmented generation for biological knowledge bases.
Limitations of LLMs in scientific reasoning.
Introduction to protein language models.
AI-assisted annotation of biological concepts.
Comparing embeddings for scientific abstracts.
A small question-answering demo over public biology papers.
Hallucination risk in AI-generated scientific summaries.
How to evaluate AI tools used for biology research support.
Ethical and practical issues in AI-assisted biomedical workflows.
Research Note Standards
Research notes and portfolio artifacts should explain what was learned, support factual claims with sources, and make limitations clear. Reviewed work will be linked here with author credit.
Before Posting
- Factual claims are sourced, cited, or qualified.
- Papers, datasets, images, code, and external resources are attributed.
- Meaningful AI assistance is disclosed where appropriate.
- Limitations, assumptions, and uncertainties are stated.
Excluded Content
- No medical advice, diagnosis, or treatment recommendations.
- No private, sensitive, proprietary, regulated, or restricted data.
- No confidential business projects or operational praxagent work.
- No client, customer, sales, administrative, or required marketing work.
Portfolio status: Work is in progress. Reviewed projects
will be added here with credit to Huriah Fitzgerald, Class of 2026, University of Delaware.