library(readxl)
library(plotly)
library(tidyverse)
library(htmlwidgets)

Introduction

Generative artificial intelligence has quickly become part of university learning. Students now use AI tools to explain difficult concepts, summarise readings, generate ideas, improve writing and prepare assessed work. However, the most important question is not only whether students are using AI, but whether they are benefiting equally and using it critically.

This visual story explores the rise of AI use among students, the academic tasks students use it for, the reasons they are drawn to it, the concerns that limit its use, and the emerging gap between the importance of AI skills and the support students receive. The story argues that the real educational divide may not be between students who use AI and those who do not, but between students who can use AI responsibly and students who rely on it without enough guidance.

chart1 <- read_excel("data/chart1_real.xlsx")
chart2 <- read_excel("data/chart2_real.xlsx")
chart3 <- read_excel("data/chart3_real.xlsx")
chart4 <- read_excel("data/chart4_real.xlsx")
chart5 <- read_excel("data/chart5_real.xlsx")

Chart 1: From Novelty to Necessity

p1 <- plot_ly(
  chart1,
  x = ~Year,
  y = ~AI_Usage,
  type = "scatter",
  mode = "lines+markers",
  text = ~paste("Year:", Year, "<br>Students using AI:", AI_Usage, "%"),
  hoverinfo = "text"
) %>%
  layout(
    title = "AI adoption among students has become near universal",
    xaxis = list(title = "Year"),
    yaxis = list(title = "Students using AI (%)", range = c(0, 100))
  )

p1

AI adoption among university students has increased rapidly. HEPI’s student surveys show that the proportion of students using AI rose from 66% in 2024 to 92% in 2025 and 95% in 2026. This suggests that AI has moved beyond being a novelty and has become a mainstream part of student life. The speed of this growth creates a challenge for universities: students are already using AI, so institutions need to focus on guidance, literacy and responsible use rather than treating AI as an optional or future issue.

Chart 2: How Students Use Generative AI for Assessment

p2 <- plot_ly(
  chart2,
  x = ~Percentage,
  y = ~reorder(Activity, Percentage),
  type = "bar",
  orientation = "h",
  text = ~paste(Percentage, "%"),
  textposition = "auto",
  hovertext = ~paste(Activity, "<br>Percentage:", Percentage, "%"),
  hoverinfo = "text"
) %>%
  layout(
    title = "Students use AI for different assessment tasks",
    xaxis = list(title = "Percentage of students (%)", range = c(0, 100)),
    yaxis = list(title = "")
  )

p2

Students are not using AI in only one way. The 2025 HEPI survey shows that students commonly use generative AI to explain concepts, summarise articles, suggest research ideas and structure their thoughts. These uses indicate that AI is being integrated into the learning process, not just used at the final stage of assessment. This matters because it shows AI can act as a study assistant, but it also raises questions about how much of the learning process is still being completed independently by students.

Chart 3: Why Students Use AI

p3 <- plot_ly(
  chart3,
  x = ~Percentage,
  y = ~reorder(`Reason for Using AI`, Percentage),
  type = "bar",
  orientation = "h",
  text = ~paste(Percentage, "%"),
  textposition = "auto",
  hovertext = ~paste(`Reason for Using AI`, "<br>Percentage:", Percentage, "%"),
  hoverinfo = "text"
) %>%
  layout(
    title = "The main attraction of AI is efficiency and support",
    xaxis = list(title = "Percentage of students (%)", range = c(0, 100)),
    yaxis = list(title = "")
  )

p3

The strongest reasons students use AI are practical. Many students use AI because it saves time, improves the quality of their work and provides instant support. This helps explain why adoption has grown so quickly. AI can provide help outside traditional study hours and can make support feel more personalised. However, this also creates a productivity paradox: the same features that make AI useful may also make students more dependent on it, especially when they are under pressure.

Chart 4: What Puts Students Off Using AI

p4 <- plot_ly(
  chart4,
  x = ~Percentage,
  y = ~reorder(Concern, Percentage),
  type = "bar",
  orientation = "h",
  text = ~paste(Percentage, "%"),
  textposition = "auto",
  hovertext = ~paste(Concern, "<br>Percentage:", Percentage, "%"),
  hoverinfo = "text"
) %>%
  layout(
    title = "Students remain concerned about misconduct, accuracy and fairness",
    xaxis = list(title = "Percentage of students (%)", range = c(0, 100)),
    yaxis = list(title = "")
  )

p4

Students are enthusiastic about AI, but they are not unconcerned. The 2025 HEPI survey shows that students are most discouraged by the risk of being accused of cheating and by the possibility of false results or hallucinations. Bias, institutional rules, data privacy and fairness also shape student behaviour. These concerns show that AI use is not simply a technology issue. It is also an academic integrity, trust and equity issue. Clearer guidance from universities is therefore essential.

Chart 5: The Future Divide: AI Skills and Institutional Support

p5 <- plot_ly(
  chart5,
  x = ~Percentage,
  y = ~reorder(`AI Future Readiness Indicator`, Percentage),
  type = "bar",
  orientation = "h",
  text = ~paste(Percentage, "%"),
  textposition = "auto",
  hovertext = ~paste(`AI Future Readiness Indicator`, "<br>Percentage:", Percentage, "%"),
  hoverinfo = "text"
) %>%
  layout(
    title = "Students value AI skills, but support has not fully caught up",
    xaxis = list(title = "Percentage of students (%)", range = c(0, 100)),
    yaxis = list(title = "")
  )

p5

The future divide is not only about whether students can access AI tools. It is also about whether they are supported to use those tools effectively. The 2026 HEPI survey shows that most students believe AI skills are essential to thrive, but fewer feel that teaching staff are helping them develop these skills for future careers. This gap suggests that universities need to embed AI literacy more clearly into teaching, assessment and career preparation.

Conclusion

The evidence shows that generative AI is now a normal part of university life. Student use has grown rapidly, and AI is being used for a wide range of learning and assessment activities. Students are drawn to AI because it saves time, improves work quality and provides immediate support. At the same time, they remain worried about academic misconduct, hallucinations, bias, privacy and fairness.

The key issue is therefore not whether AI should exist in higher education. Students are already using it. The more important question is whether universities can help students use AI critically, ethically and effectively. If institutions do not provide clear guidance and skill development, AI may deepen educational inequalities between students who know how to use it well and those who rely on it without understanding its limits.

The future of student learning will depend less on access to AI and more on AI literacy. Students need to learn how to question AI outputs, understand risks, protect academic integrity and use AI as a support tool rather than a substitute for thinking. This is where universities have an important role to play.

References

Freeman, J. (2025). Student generative AI survey 2025. Higher Education Policy Institute.

Stephenson, R., & Armstrong, C. (2026). Student generative AI survey 2026. Higher Education Policy Institute.

World Economic Forum. (2025). The future of jobs report 2025. World Economic Forum.

Acknowledgements

I used RStudio, R Markdown, Plotly and the readxl package to create the interactive visualisations. I used generative AI assistance to help structure the story, organise the narrative flow and troubleshoot coding issues. The data values used in the charts were taken from the HEPI Student Generative AI Survey 2025 and HEPI Student Generative AI Survey 2026 reports.