Melissa Clucas Walter
Assistant Professor
School of Clinical and Developmental Sciences
College of Health and Human Sciences
Course(s) Targeted by this Innovation
HDFS 230: Child Development
Development and Implementation
2026-2027
Purpose and Impact
HDFS 230 is an important candidate for redesign because it is a high‑enrollment General Education course that also serves as a gateway prerequisite for multiple HDFS, Early Childhood Education, and human‑services majors.
Students in this course often encounter challenges applying developmental concepts across time or connecting theory to real‑world practice. Integrating AI into the course through a guided, longitudinal child simulation directly addresses these challenges by giving students a coherent developmental case study that grows with them throughout the semester. This allows students to see how early experiences, family context, and developmental domains interact over time.
AI integration enhances learning by generating weekly developmental “snapshots” that students must analyze, correct, and expand using the textbook and course concepts. This redesign strengthens conceptual understanding, supports applied reasoning, and builds ethical and professional communication skills while also advancing students’ AI literacy and preparing them for a workforce where AI‑generated information will increasingly intersect with child‑ and family‑serving professions.
This innovation also offers significant impact and scalability. It creates a replicable model for responsible AI integration in human development courses and can be adapted for psychology, early childhood education, social work, nursing, family sciences, and teacher preparation programs. The use of an OER textbook and free AI tools removes financial barriers and supports equitable access. Students become invested in “their child,” increasing motivation and retention, while weekly tasks promote higher‑order thinking, analysis, evaluation, synthesis, and professional judgment.
Description of Innovation
This redesign incorporates AI into HDFS 230 through a semester‑long, guided child‑development simulation that replaces commercial tools like My Virtual Child with a free, customizable, and pedagogically richer alternative. Students begin by creating a fictional child and then build a narrated PowerPoint portfolio that follows the child from birth through adolescence. Each week, students upload their evolving portfolio to an AI tool, which uses the child’s full history and specific prompts to generate a developmentally relevant “snapshot” aligned with the week’s topic (e.g., cognitive development in infancy, social‑emotional development in early childhood). Students then complete a structured four‑part analysis including developmental evaluation, caregiving decisions, family communication, and ethical reasoning using the textbook and course concepts to correct inaccuracies, identify bias, and apply evidence‑based developmental science.
AI is used intentionally and responsibly, as it provides the raw scenario, while students perform the higher‑order thinking. This design enhances learning by giving students a coherent, longitudinal case study that makes developmental concepts concrete and connected across time. It also builds essential AI literacy by teaching students how to evaluate AI outputs, recognize limitations, and make informed, ethical decisions.
The narrated portfolio ensures authenticity and supports communication skills, while the weekly structure keeps the work manageable, engaging, and deeply aligned with course outcomes. Project deliverables include a complete set of weekly AI prompts aligned with textbook chapters, a narrated PowerPoint portfolio template, student‑facing instructions for responsible AI use, rubrics for developmental analysis and professional communication, and a faculty reflection on implementation and student outcomes.
Anticipated outcomes include improved student ability to apply developmental theory to realistic scenarios, identify and correct AI‑generated inaccuracies, communicate effectively with families, recognize bias and practice ethical reasoning, understand development longitudinally, and use AI responsibly and critically.