Learning with Artificial Intelligence: Humans Versus AI and Internet of
Things
Chuck Robertson, Michele Hill, Gabi Faulkner, Ava Andrick, Carson Sutherland, & Harley
Ansbro
Introduction
Artificial Intelligence (AI) has evolved and drastically changed how people work, study and
learn, and live their day-to-day lives. Specifically, students and faculty are increasingly using AI for
understanding complex topics, finding evidence in research endeavors, and editing and revising their
writing (Black & Tomlinson, 2025). This study investigated several forms of AI (Conversational and
Generative) to see how students might learn similarly to studying with a friend in class. Generative AI
(ChatGPT and Gemini) has primarily been defined by its ability to perform functions that are traditionally
associated with humans, such as decision-making, learning, and completing simple/complex tasks
(Collins et al., 2021). Assistive AI, Internet of Things (IoT) appliances are consumer AI devices that
gather and analyze data together to monitor/control environments, while also achieving common goals
(Choudhary, 2024). The purpose of this research was to compare how well students learn with different
types of AI-enabled systems compared to a human study partner (study buddy). A traditional pre-test
post-test design with general trivia questions was the comparison task.
Hypotheses
● A main effect of studying, participants' post-test scores will be significantly higher than pre-test
scores.
● A main effect of study partner, with not all partners showing the same gains.
● A significant interaction with learning gain differences based on the type of study partner (Study
Buddy, ChatGPT, Gemini, Alexa, Google Home).
Methods
Participants
● Participants were 100 undergraduate students (age range = 18–40 years, M = 20.12, SD = 3.02).
The sample included 60 females, 39 males, and 1 participant identifying as other.
● Most participants identified as White (n = 84), with smaller proportions identifying as
Hispanic/Latino (n = 8), African American (n = 3), Asian (n = 2), or multiracial (n = 2).
● Participants represented a range of majors, with psychology being the largest group (n = 43),
followed by biology (n = 6), cybersecurity (n = 5), business (n = 5), kinesiology (n = 5), criminal
justice (n = 4), and other majors (n = 32).