bioRxiv Science⌕ Search
Home/Archive/bioRxiv

bioRxiv · 10.1101/2024.12.19.629465

Development of Learning Objectives for Non-Major Introductory Biology Using a Delphi Method

Brickman, P.·Gormally, C.
View original source ↗Export metadata ↓

Abstract

Biology faculty have consensus-based guidelines based on Vision and Change principles about what to teach introductory biology majors. In contrast, faculty have not reached a consensus concerning the learning goals for introductory non-majors courses. Yet, more than 8 out of 10 undergraduates are not science majors. The goal of this study was to develop and evaluate learning objectives for non-majors introductory biology courses. We performed a modified-Delphi study of learning objectives (LOs) for non-majors biology. We engaged a total of 38 biology faculty experts from institutions across the US in three iterative rounds to identify, rate, discuss, and re-rate >300 LOs for non-majors biology courses. Faculty provided feedback to determine whether the LOs are critical for students to learn and if the LOs encompass what students need to learn about this issue, as well as if anything is missing. As a result of expert evaluation, 60.7% of LOs (164) were deemed critical. Experts also suggested 22 additional new LOs.

Explore related subjects

scientific communication and educationPreprint

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Brickman, P., Gormally, C.. 2024-12-20. Development of Learning Objectives for Non-Major Introductory Biology Using a Delphi Method. https://doi.org/10.1101/2024.12.19.629465

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Evaluating Large Language Models as Tools to Navigate Researchers in Rapidly Evolving Research Landscapes: A Case Study in Cancer Drug Response Prediction

Large Language Models (LLMs) have emerged as promising tools for assisting researchers in automating and accelerating the synthesis of literature reviews. However, their reliability is a significant concern due to issues like factual inaccuracies and hallucinations. The key question is whether LLMs can reliably provide comprehensive, up-to-date overviews and analyses. This study evaluates the performance of three leading LLMs (OpenAI's ChatGPT, Google's Gemini, and DeepSeek) on the complex task of generating a comprehensive survey paper on deep learning for cancer Drug Response Prediction (DRP). By testing both standard and Deep Research (DR) / Deep Think (DT) modes of LLMs with prompts of varying detail, this paper assesses key academic dimensions, including reference management, content quality, and analytical depth. Key findings reveal that while DR modes of LLMs significantly improve reliability by eliminating hallucinations, performance variations exist across models and prompts. A trade-off between reference quantity and integration quality was observed, and even the best-performing models lacked the analytical depth of human experts, often requiring extensive human supervision. The study concludes that LLMs currently serve as powerful assistive tools but still cannot replace the critical validation and synthesis provided by human researchers. Choosing the best LLM to use depends on the task in hand, while several strategies can be implemented to improve the produced output.

scientific communication and education↗
bioRxiv2026

A strong start for sustained success: inclusivity through a national group mentorship program for first-year graduate students

In the United States, STEM graduate programs and workforce do not represent the demographics of the population. Obstacles, including a lack of transparency, community, and accessible information in navigating academia, disproportionately affect students from underserved backgrounds. Peer mentoring networks can address these disparities. Here, we describe Cientifico Latino, Inc.s Graduate Student Engagement and Community (CL-GSEC) program, a nationwide, group-based peer mentorship program that has served first-year graduate students across the U.S., especially those from underserved backgrounds. Surveys indicate CL-GSEC positively impacts the first-year graduate experience. We highlight key program features, challenges, and insights, such as financial strains faced by first-year graduate students. We offer suggestions for how faculty and departments can better support students during this critical early stage of graduate training. We hope that reporting on CL-GSECs program structure, evaluations, and findings will guide educational leaders in expanding programming for junior graduate students.

scientific communication and education↗
bioRxiv2026

Biodesign Buddy: Integrating Generative Artificial Intelligence in Academic Biodesign

Biodesign is an interdisciplinary research domain that incorporates principles from design and the life sciences to develop new systems, processes, and objects. Collegiate biodesign educators face unique pedagogical challenges, including an absence of relevant scholarship on curriculum design and instructional best practices for cultivating student scientific literacy. These difficulties may be overcome with newly available technologies, like generative AI systems, that enable personalized learning through domain-specific semantic spaces. This article examines the instructional value of one such domain-specific LLM, Biodesign Buddy, through a mixed-methods analysis of an eight-week study involving 64 students participating in an international biodesign competition. Results indicate strong support for integrating AI into biodesign coursework. Surveys captured attitudes toward AI, scientific literature, and learning experiences to assess AIs impact on learning outcomes. Findings suggest that integrating AI into biodesign pedagogy can meaningfully redress conceptual issues in biodesign while informing broader debates on AIs role in higher education. Impact StatementThis article introduces Biodesign Buddy, a domain-specific generative AI system for collegiate biodesign education, and reports on its exploratory deployment, offering design principles and preliminary findings to inform the development of AI-supported pedagogies for interdisciplinary biodesign instruction.

scientific communication and education↗