##Introduction
Artificial Intelligence tools are becoming increasingly common in education, providing students with support for learning, research, writing, coding and assessment preparation. This project explores how students use AI tools, the purposes for which they are adopted, their impact on learning outcomes and the ethical concerns associated with their use. Using a dataset of 50,000 student records, the visualisation reveal patterns of AI adoption, academic performance , trust, satisfaction, and future intentions regarding AI us in education.
##Chart 1
tool_count <- ai_data %>%
count(ai_tool, sort = TRUE)
p1 <- ggplot(tool_count,
aes(x = reorder(ai_tool, n),
y = n)) +
geom_col(fill = "#D95F02", width = 0.75) +
geom_text(aes(label = n),
hjust = -0.25,
size = 4) +
coord_flip() +
expand_limits(y = max(tool_count$n) * 1.08) +
labs(
title = "Students Adopt a Diverse Range of AI Tools",
subtitle = "Usage patterns across 50,000 student records",
x = "AI Tool",
y = "Number of Users"
) +
theme_minimal()
ggplotly(p1)The chart shows that students use a wide variety of AI tools for learning and academic support. While Quiz Generators and Assessment Assistants have slightly higher usage, adoption is relatively balanced across all tools. This suggests that students rely on multiple AI technologies rather than a single platform.
##Chart 2
purpose_subject <- ai_data %>%
count(subject_area, ai_use_purpose)
p2 <- ggplot(
purpose_subject,
aes(
x = subject_area,
y = n,
fill = ai_use_purpose
)
) +
geom_col(position = "stack") +
labs(
title = "AI Usage Purposes Across Academic Subjects",
subtitle = "Different disciplines use AI for different learning activities",
x = "Subject Area",
y = "Number of Students",
fill = "AI Use Purpose"
) +
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(
face = "bold",
size = 16,
hjust = 0.5
),
axis.text.x = element_text(
angle = 45,
hjust = 1
)
)
ggplotly(p2)AI is used across all academic disciplines, but the purposes of use vary. Students employ AI for activities such as summarisation, brainstorming, organisation, and learning support. The distribution suggests that AI tools have become embedded in a wide range of educational contexts rather than being limited to specific subject areas.
##Chart 3
library(tidyverse)
library(plotly)
ai_data <- read.csv("AI Tools Adoption.csv")
p3 <- ggplot(
ai_data,
aes(
x = pre_test_score,
y = post_test_score,
color = improvement
)
) +
geom_point(alpha = 0.4) +
labs(
title = "Students Show Learning Improvements After Using AI Tools",
subtitle = "Comparison of pre-test and post-test scores",
x = "Pre-Test Score",
y = "Post-Test Score",
color = "Improvement"
) +
theme_minimal(base_size = 12)
ggplotly(p3)The chart indicates a positive relationship between pre-test and post-test scores. Students generally achieve higher post-test scores after using AI-supported learning tools, suggesting that AI can contribute to improved academic performance and learning outcomes.
##Chart 4
p4 <- ggplot(
ai_data,
aes(
x = trust_score,
y = satisfaction_score,
color = perceived_usefulness
)
) +
geom_point(alpha = 0.4) +
labs(
title = "Higher Trust in AI Is Associated with Greater Satisfaction",
subtitle = "Relationship between trust, satisfaction and perceived usefulness",
x = "Trust Score",
y = "Satisfaction Score",
color = "Perceived Usefulness"
) +
theme_minimal(base_size = 12) +
theme(
plot.title = element_text(
face = "bold",
size = 16,
hjust = 0.5
)
)
ggplotly(p4)