Enhancing Constructivist Learning: The Role of Generative AI in

Personalised Learning Experiences

Hua Guo

, Weiqian Yi

and Kecheng Liu

School of Electronic Engineering and Computer Science, Queen Mary University of London, London, U.K.

Harrow School, Druries House, London, U.K.

Informatics Research Centre, University of Reading, Reading, U.K.

Keywords: Generative AI & AI-Generated Content in Higher Education, Constructivist Learning, Authentic Learning.

Abstract: This paper explores the transformative role of generative AI in enhancing constructivist learning, where

students actively construct knowledge through meaningful experiences. By investigating the synergies

between generative AI and constructivist learning, the study uncovers how AI fosters personalized educational

experiences. The research underscores the profound influence of generative AI on constructivist learning,

empowering students to become active, motivated, and lifelong learners by tailoring their education, fostering

creativity and collaboration, and upholding ethical principles. The study advocates for the responsible and

purposeful integration of generative AI, which would revolutionize education and prepare students for future

challenges.

1 INTRODUCTION

Generative AI represents a tipping point in the

development of AI with huge adoptions from various

industrial users. It has the potential to change the way

we interact with and leverage artificial intelligence in

our daily life, revolutionising how we access

information, use information, reshape the learning

curve and solve problems.

In higher education, generative AI tools are

widely adopted by students. Cassidy (2023) statistics

shows that one-fifth of students using AI programs in

assessment tasks just two months after Chat-GPT

release. Another survey conducted in January 2023

(Intelligent, 2023) reported that over one-third were

using Chat-GPT for assessment writing. Of these

students, 75% thought it counted as cheating but did

so anyway. Furthermore, over 40 per cent of

universities are investigating students for using

ChatGPT to cheat (Snepvangers, 2023). Based on the

above research, it is apparent that ChatGPT has

garnered significant adoption among college students

and lecturers urged to review assessments in the UK

amid concerns over the new AI tool (Weale, 2023).

The question for universities is how to use generated

AI tools safely, effectively, and appropriately, rather

than ban them from students. Consequently, it

becomes imperative to engage in a thoughtful

discussion regarding the pedagogical applications of

AI.

This paper will focus on the applications of

generative AI in higher education, along with

pedagogical methods, to explore how generative AI

enhance teaching and learning.

2 CONSTRUCTIVIST LEARNING

THEORY AND ITS

SIGNIFICANCE IN HIGH

EDUCATION

Constructivism is a learning theory that has

significant implications for higher education. It

emphasizes the learner's active role in constructing

understanding and knowledge through meaningful

experiences and interactions. In the context of higher

education, constructivism shifts the focus from

passive knowledge transmission to active

engagement and critical thinking.

Key principles of constructivism in higher

education:

 Active Learning: A subject process.

Constructivism promotes active learning

Guo, H., Yi, W. and Liu, K.

Enhancing Constructivist Learning: The Role of Generative AI in Personalised Learning Experiences.

DOI: 10.5220/0012688700003690

In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 1, pages 767-770

ISBN: 978-989-758-692-7; ISSN: 2184-4992

767

strategies such as discussions, problem-

solving, projects, and hands-on experiences.

Students are encouraged to explore and

discover knowledge rather than passively

receiving information.

 Prior Knowledge: Prior knowledge and

experiences play a crucial role in the learning

process. Educators recognize and build upon

students' existing knowledge, integrating new

information with their mental frameworks.

 Social Interaction: Social interactions and

collaborative learning are essential in

constructivist classrooms. Students engage in

discussions, debates, and group work to co-

construct knowledge through dialogue and

shared experiences.

 Student-Centred Approach: Constructivism

places students at the centre of the learning

process. It tailors instruction to individual

needs, interests, and learning styles, fostering a

more personalized and meaningful learning

experience.

 Reflection and Metacognition: Students are

encouraged to reflect on their learning process

and think metacognitively about their own

thinking. Self-assessment and self-regulation

of learning are integral to the constructivist

approach.

 Real-World Relevance: Constructivist

educators aim to connect classroom learning to

real-world contexts and applications, making

the learning experience more authentic and

meaningful.

In line with the principles of constructivism, the

learning process is viewed as a journey of knowledge

construction. Through comprehending both the sign

and its meaning, individuals can effectively apply the

acquired knowledge (as shown in Figure 1).

Figure 1: Learning as knowledge construction from

semiotics (modified (Liu and Li, 2015)).

As knowledge construction is a subjective

process, there must be a gap between the teacher and

the learner. As shown in Figure 2, the learning

process is a sense-making process navigated by prior

knowledge. As the students have different knowledge

contexts from their lecturers, the gap generates and

hinders quality learning.

Figure 2: The gap between teacher and student knowledge

understanding generated by different prior knowledge

(modified (Liu and Li, 2015)).

Concerning the subjective nature of the learning

process, generative AI tools serve as ideal adaptive

assistants for students acquiring new knowledge,

responding to their personalized questions with

precision.

The next section explores the various approaches

of integrating generative AI applications in

educational settings.

3 INTEGRATING GENERATIVE

AI IN HIGHER EDUCATION

Generative AI tools play a pivotal role in facilitating

adaptive learning through personalised feedback,

supporting research and data analysis, automating

administrative service, and contributing to the

development of innovative assessments (Rasul et al.,

2023). This paper focuses on the following four

specific tools: Chat-GPT(Rudolph et al., 2023), Bing

Chat, Bard, Ernie (Teubner et al., 2023), with the

objective of exploring the synergies between

constructivist learning and generative AI.

3.1 Personalised Learning with

Generative AI

Personalised learning has the potential to address the

longstanding challenges in education, such as

addressing learning gaps, accommodating diverse

learning styles, and promoting students’ engagement.

Through the utilization of generative AI embedding

alongside adaptive learning algorithms, instructors

have the ability to customize education, feedback, and

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assistance to match the individual learning profile of

each student. This component of the investigation

will specifically examine three perspectives:

 Adaptive content generation and customised

learning pathways

 AI-driven individualised feedback and support

 The role of generative AI in catering to diverse

learning styles

3.2 Fostering Engagement and

Creativity with AI-Generated

Content

AI-driven creative content includes a diverse array of

educational resources, such as interactive

simulations, virtual reality experiences, adaptive

learning modules, personalised quizzes and

assessments, and AI-generated educational games

and activities. These customised instructional

resources can boost student motivation by offering

individualised and engaging learning opportunities

that captivate individuals' interests and cultivate their

inherent drive to learn. In this section, the following

three perspectives will be investigated.

 AI-Powered Creative Content and Its Impact

on Student Motivation

 Using AI for Dynamic Simulations and Real-

World Problem-Solving

 Augmented Reality (AR) and Virtual Reality

(VR) Applications in Constructivist Learning

3.3 Collaborative Learning

Empowered by Generative AI

Collaborative learning environments can enhance the

effectiveness and efficiency of group-based/cohort-

based learning activities. When generative AI can be

applied into collaborative learning settings, the

learning processes can be optimised for individual

needs, and knowledge synthesis facilitated by AI can

enhance the personalised learning experiences. This

section will dive into the following three perspectives

to explore the role of generative AI in project support,

interaction and feedback, as well as problem solving

activities.

 AI-Driven Group Formation and Collaborative

Project Support

 Facilitating Peer Interaction and Constructivist

Feedback

 Leveraging AI for Collaborative Problem-

Solving Activities

3.4 Ethical Considerations and

Challenges in AI Integration

As generative AI tools have been widely adopted by

students in higher education, there is an urgent need

to provide guidelines on how to use AI in their

learning processes and to ensure equitable use of

these technologies in educational environments. This

section will explore the potential challenges of

integrating AI into educational systems from the

following three points.

 Ensuring Ethical Use of AI in Education

 Addressing Bias and Fairness in AI Algorithms

 Privacy and Security Concerns in AI-Driven

Educational Environments

Challenges in Implementing Generative

AI for Constructivist Learning

By examining the challenges, educators and

researchers can identify potential barriers and

obstacles that may hinder the effective

implementation of Generative AI in constructivist

learning environments. The challenges mainly come

from the privacy and data security concerns and the

ethical issues.

 Ethical Considerations and Bias in AI

Algorithms

 Addressing Privacy and Data Security

Concerns

 Empowering Educators to Leverage AI

Effectively

4 A PILOT RESEARCH ON

STUDENT USE OF

GENERATIVE AI

Understanding the gaps in teaching and learning, as

well as the difficulties students encounter in their

learning process, is essential for formulating effective

questions that promote personalised learning. Figure

3 depicts the implementation of the constructivist

learning concept in practical teaching.

We are currently developing a tool designed to

identify AI-generated content within students' essays.

Due to privacy and ethical considerations, it is not

feasible for educators to upload students' essays to

third-party tools. This tool will serve to pinpoint

sentences or paragraphs that have been generated by

AI algorithms. By analysing this content, educators

can gain insights into the challenges students face

Enhancing Constructivist Learning: The Role of Generative AI in Personalised Learning Experiences

769

Figure 3: The process of applying constructivist learning.

when writing essays. For instance, students may

struggle with defining concepts clearly or providing

precise application scenarios. Additionally, the tool

will aid in identifying areas where students may

require further guidance or support in their writing

process.

Figure 4: Content identification process.

As shown in Figure 4, a pre-trained text-

generation transformer model, such as GPT-2

(Lysandre, 2023) can be fine-tuned separately with

human essays and machine-generated essays. For a

test human essay, the transformer trained on human

essays should be able to predict words in the essay

more accurately. Transformers can be further fine-

tuned on a specific topic or author, giving more

accurate results.

An example dataset is available on Kaggle

(https://www.kaggle.com/competitions/llm-detect-

ai-generated-text/data), containing human and LLM

generated essays. Academic papers are widely

available on the internet, while LLM essays can be

generated with the ChatGPT API.

5 DISCUSSION AND

CONCLUSIONS

Examining the adoption of generative AI tools in

curriculum design reveals key findings that enhance

understanding of the role of AI tools in teaching and

learning activities. This contributes to achieving

constructivist alignment by clarifying the needs of

students with diversified backgrounds. Furthermore,

exploring the implications and future applications of

AI in higher education elucidates its potential to

foster comprehensive and constructivist learning

experiences.

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