Graduate Certificate in Mind, Brain & Education Curriculum & Admission Requirements


PROGRAM STRUCTURE

Coursework

The certificate consists of four online courses (3 required, 1 elective). Coursework will draw on basic and applied interdisciplinary research from the disciplines of neuroscience, psychology, cognitive science, and artificial intelligence in order to inform educational pedagogy through research, collaboration, and advocacy. The timeframe for completion is two semesters (one academic year) or four semesters (two academic years), depending on the number of courses taken per semester.

Course Requirements

This graduate-level course is designed to provide an overview of important models, principles, and research findings related to the chronological sequence of cognitive, physical, social, and emotional development through eight major age periods: infancy and toddlerhood (the first two years), early childhood (two to six years), middle childhood (six to eleven years), adolescence, early adulthood, middle adulthood, late adulthood, and the end of life. This chronological organization allows you to explore and apply theories covering several age periods to each and to remember how different domains of development evolve simultaneously and impact one another throughout the lifespan. Additionally, the course examines how artificial intelligence technologies influence developmental trajectories across these life stages, from AI-enhanced learning tools in early childhood to assistive technologies in late adulthood, providing insights into the bidirectional relationship between technological innovation and human development in contemporary society.

This graduate-level course provides an overview of important models, principles, and research findings related to the learning process. Attention is given to learning and information processing theories that explain perceptual behavior, verbal learning, memory, and social learning processes, alongside computational models of cognition and machine learning paradigms. Emphasis is placed on student development of research proposals in human learning and achievement, including explorations of how AI systems emulate, enhance, or transform traditional learning frameworks. The course examines the parallels between human cognitive architectures and artificial neural networks, exploring the theoretical foundations that unite biological and artificial learning systems.

This graduate-level course emphasizes the integration of knowledge in understanding human behavior holistically. The course material addresses the neural, physiological, and genetic foundations of human affective and cognitive development and experience. Attention is given to how the nervous system behaves and integrates with other brain structures that support fundamental mental abilities such as attention, cognitive control, affective, language, memory, and social cognition. The emergence of artificial intelligence systems provides a compelling computational framework for modeling and testing theories of neural processing and cognitive function. Also, attention is given to the basic structure of the nervous system, the fundamental units of the nervous system (the neuron and the synapse), the function of different components of the central nervous system, the sensory, motor, and memory systems, and the networking of these systems in perception, learning, and cognition. Finally, within these topics, attention is given to understanding how the nervous system works by studying the physiological basis of adaptive behaviors and maladaptive behaviors of the mind.

and

Using a neuroscience lens, this course focuses on the integration of research and theory on motivation and emotion from across developmental, affective, cognitive, and social fields, enriched by insights from artificial intelligence. Emotion and motivation are the core of human life and thought, without which we could not survive even in the most benign environments. This course examines how emotion and motivation innervate thought and behavior to enrich human experience and 'bias' the decisions we make in everyday life, as well as how thought, behavior, and environment innervate emotion and motivation. Additionally, the course explores how artificial intelligence models are being developed to understand, simulate, and respond to human emotional and motivational states, providing new methodological approaches to investigate these fundamental psychological processes.

or

This graduate-level course explores the neurodevelopment of executive function (EF) across the lifespan, underscoring its vital role in daily life, while examining how artificial intelligence tools can both model and support these cognitive processes. The developmental trajectory of EF commences in infancy and typically shows improvements in effectiveness and efficiency during childhood, adolescence, and early adulthood. This EF progression often plateaus, followed by stasis, before experiencing a relative decline in later years. Artificial intelligence systems, which increasingly mirror aspects of human executive functioning through computational models, offer new insights into these developmental patterns and potential interventions. Nevertheless, executive function should not be viewed as a unitary construct; it encompasses distinct cognitive components, each displaying unique developmental patterns that are malleable throughout the lifespan and can be differentially augmented through AI-based cognitive training applications. This course will explore the development, maintenance, and decline of executive function across the lifespan, alongside examining how artificial intelligence both informs our understanding of EF and provides novel approaches for assessment and enhancement.

Additional Requirements

  • Minimum program credit hours: 12


Graduate Admission

Applicants must have a bachelor’s degree with at least a 3.0 grade point average on a 4.0 scale. Meeting minimum admission standards does not guarantee admission to the program. 


Application Deadlines

Dec. 16, 2024
Spring semester application deadline
May 1, 2025
Summer semester application deadline
Aug. 1, 2025
Fall semester application deadline


Program Faculty