Tag: Integrating

AI meets the VLE: integrating an AI assistant at Saïd Business School, University of Oxford

This blog was kindly authored by Melissa Bowden, Senior Content Writer and Editor at Kortext.

Artificial intelligence (AI) is no longer a distant concept in higher education. It’s here, and it’s already transforming the way institutions are delivering learning.

At Oxford University’s Saïd Business School, an exciting new pilot is underway: a VLE-integrated AI assistant developed in collaboration with Kortext, Microsoft and Instructure. This initiative is more than a technology trial; it’s a strategic step towards realising Oxford’s ambitious AI vision.

Starting the transformation journey

The University of Oxford is a complex, devolved organisation with 26,500 students and 16,500 staff. The potential applications of AI across this ecosystem are vast – from accelerating research and processing data to enhancing student engagement and streamlining staff workflows. But with so many possibilities, the question is: where do you start?

For Mark Bramwell, Chief Digital & Information Officer at Saïd Business School and Director of Strategic Digital Partnerships at the University of Oxford, one answer lies in the VLE. Integrating an AI assistant into their Canvas instance is a practical first step in a broader digital transformation strategy focused on agility, data and world-leading innovation.

As Mark explains:

AI will be core to our future. We need to equip our faculty, researchers, students and staff with the latest technologies – not just to make them more efficient today, but to also ensure they’re fully prepared with essential skills they’ll need in the workplace.

The power of partnership

Saïd Business School has been working in collaboration with Kortext, Microsoft and Instructure on the Canvas-integrated AI assistant pilot as part of its existing partnership.

When establishing the pilot, there were three non-negotiables for Bramwell. The AI assistant must be seamlessly integrated into a student’s learning journey, use a ring-fenced secure data environment, and be interoperable with existing technologies at Oxford.

With Canvas as the delivery platform, the AI assistant is available to students and faculty within their existing learning environment. All data is stored safely within the university’s domain and tenancy, complying with regulatory requirements. Finally, the pilot builds on Oxford’s long-standing strategic partnership with Microsoft as a natural evolution of its digital ecosystem.

The pilot will span 1,200 students across all degree programmes, alongside faculty and instructional designers. For Bramwell, this project is:

an exciting extension of our digital strategy and AI activities, leveraging the synergies that exist between our three tech collaborators.

Data-driven insights for smarter course design

It’s early days, but the pilot’s outcomes are greatly and positively anticipated. For Bramwell, one of the most beneficial aspects is the AI assistant’s ability to capture granular engagement data.

Which content are students interacting with? Where are they disengaging? These insights can inform continuous improvement in course design and content strategy, enabling faculty to create responsive programmes relevant to student needs. They can also accelerate course development, with staff expertise complemented by AI-enabled tools and recommendations.

Competitive advantage in a global market

Higher education is an increasingly competitive sector, both in the UK and globally. Within this context, one of Saïd Business School’s ambitions is clear: to extend its reach through online learning and deliver an experience that reflects the name, brand and value of the University of Oxford.

For Bramwell, personalised learning, enriched by data and analytics, is central to that differentiation.

Our job is to make every learner better equipped for the world and the future of work than when they joined us.

If we can do that, we’re doing our job.

Looking ahead: the future of AI-enabled education

The vision doesn’t stop there. Bramwell imagines a future where AI supports a student from ‘cradle to grave’, guiding their learning at every stage of their life.

The possibilities are endless,but must be delivered within responsible, ethical frameworks.

He also sees possibilities for global accessibility: ‘giving us the opportunity to take Oxford to learners, geographies and regions that may not have previously been possible’.

By replicating faculty expertise globally, AI can help make education more inclusive and impactful than ever before.

What happens next?

The pilot’s impact will be measured through engagement metrics, content interaction and tangible efficiency gains for both students, instructional designers and faculty. Examples here might include learners using the AI assistant to summarise reading materials and save time, or staff asking the AI assistant for content recommendations to make the student experience more engaging.

Bramwell is confident this is just the beginning. The next phase of AI-enabled learning at Oxford could involve developing truly personalised learning experiences, where learners construct and consume courses on their own terms – anytime, anywhere.

A message for higher education leaders

For other institutions, Bramwell offers a clear message:

Don’t let governance and risk paralyse innovation. Experiment, innovate and play – but do it safely. Architect your approach within secure frameworks so you can learn without compromising data or trust.

This is a pivotal moment for higher education. AI isn’t a future trend, it’s a present reality. The question now is whether institutions will embrace it proactively or be left behind.

Kortext is a HEPI Partner. Mark Bramwell is speaking at Kortext LIVE on 11 February 2026 in London. Join Mark at this free event to hear more about the pilot’s progress, the long-term vision, and why Kortext was selected as a key project partner. Find out more and secure your seat here.

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December 12, 2025
Getting it ‘right’ – a reflection on integrating Service Learning at scale into a large Faculty of Science and Engineering
This blog was kindly authored by Professor Lynne Bianchi, Vice Dean for Social Responsibility & Equality, Diversity, Inclusion and Accessibility, at the University of Manchester

I recently had the fortune to be part of a panel discussing the place of Service Learning in higher education, chaired by HEPI. My reflections before and since may inspire you to take time to think about your perspective on the nature and role of Service Learning in fast-changing university and civic landscapes. In its simplest sense, Service Learning is an educational approach that combines academic study with community service.

In my role within a large science and engineering faculty, I have rallied our staff and students to think seriously about the features, advantages and benefits of Service Learning in science and engineering contexts. For our university, this teaching and learning approach isn’t new, with expertise in the biomedical sciences and humanities teaching us much about the way in which undergraduate students can create benefit for our local communities whilst enriching their own academic experiences.

In this blog, I build on my own background as a teacher and higher education academic and draw on my experience in curriculum design when focusing on how we can provide authentic and impactful Service Learning experiences for our undergraduates.

What do we mean by the ‘right’ learning experiences?

It doesn’t take long working in this area to unearth a wide range of terms that are used interchangeably – from place-based learning, real-world learning, community-engaged learning, practice-based learning, critical urban pedagogy, industry-inspired learning and more. A gelling feature is that to get Service Learning working well there must be an authentic benefit to each party involved. The students should develop skills and understanding directly required within their degree, and the partner should have a problem explored, solved, or informed. In essence, the experience must lead to a ‘win-win’ outcome(s) to be genuine.

In our context in science and engineering, we have envisioned Service Learning working well, and considered this to include when:

For students:
- Learning has relevance: work on a project, individually or in groups, is contextualised by a problem, issue or challenge that is authentic (as opposed to hypothetical).
- Learning has resonance: developing and applying skills and knowledge to inform the problem, issue or project that dovetails with existing course specifications and requirements.
For partners:
- They are engaged: partners are involved in the design and delivery of the project to some extent. This may vary in the depth or level of engagement and requires both sides to appreciate the needs of each other.
- They are enriching: partners identify real issues that matter and expose elements of the work environment that enrich students’ awareness of the workplace and career pathways.
When is the right time for students to engage in service learning?

I am still pondering this question as there are so many variables and options that influence the choice. Which year group should service learning drop into? Or, does a developmental over time approach suit better? Is Service Learning more impactful in the later undergraduate years, or should it be an integral part of each year of their experience with us? Realistically, there won’t be a one-size-fits-all all model, and there are benefits and challenges to each. What will need to underpin whichever approach we take, will be the focused need to elicit the starting points of our students, our staff and our partners in whichever context.

Going from ‘zero to hero’ in Service Learning will require training and support for all parties. My experience working across the STEM sector for nearly three decades has taught me that no one partner is the same as another – what is a big deal to one can mean nothing to another. My thinking is that we need to see each person involved in the Service Learning experience as a core ‘partner’ and each has learning starting points, aspirations and apprehensions. Our role as programme leaders is to identify a progression model that appreciates that this is ‘learning’ and that scaffolds and key training will be required at different times – even within the process itself.

What support will be required to mobilise this model at scale?

In my early career at this university, I spent time within the Teaching & Learning Student Experience Professional Support teams, where I saw firsthand the integral way that any university programme relies on expertise in taking theoretical ideas into practice. The interplay between project management, planning, timetabling, eLearning, marketing and communications and student experience support teams, to name some, will have play such critical roles in achieving excellence in Service Learning. Working at scale in our faculty across 10 different discipline areas, will require integrated work with other faculties to harness the power of interdisciplinary projects and digital support for course delivery and assessment that can embrace an internal-external interface.

Support for scaling up will also require a culture of risk-taking to be valued and championed. Over the introductory years, we need to provide a sense of supported exploration, a culture of learning and reflection, and an ethos where failure is rarely a negative, but an opportunity. Of course, science and engineering disciplines bring with them our obligations to accrediting bodies, and a close dialogue with them about ambition, relevance and need for this enriching approach needs to be clearly articulated and agreed so that any course alteration becomes a course invigoration rather than a compromise.

Faculty culture and the way the university and the sector views and reviews SL will have a significant implication on practice and people feeling safe to innovate. As the university forges and launches its 2035 strategy the spaces for innovation and development are increasingly championed, and the months and years ahead will be ones to watch in terms of establishing a refreshed version of teaching and learning for our students.

In closing this short exploration of Service Learning, I can feel a positive tension in the air – the excitement to work together to further invigorate our student experience whilst supporting our staff and partners to embrace varied new opportunities. The ‘getting it right’ story will have many chapters, many endings as the genres, characters and plots are there for us all to create – or more pertinently ‘co-create’! What drives me most to remain in this space of uncertainty for a while longer is the anticipation of creating experiences that truly make a difference for good. As our universities transform themselves over the coming years, I invite you to join us in the dialogue and development as we have so much to learn through collaboration.
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August 29, 2025
Integrating AI into education is not as daunting as it seems
Key points:

Forty-some years ago, students sat in straight rows with books, papers, and pencils neatly lined up on their desks. But beginning in the 1990s, educators faced very different classrooms as computers found their way into schools.

For most teachers, it felt daunting to figure out how to integrate new tools into curriculum requirements–and how to find the time to make it happen. To help this digital transformation then, I joined the South Dakota Department of Education to lead summer immersion teacher training on technology integration, traveling the state to help schools understand how to use new tools like video systems. I was one of many who helped educators overcome that initial learning curve–and now tools like computers are an integral part of the education system.

Let’s face it: The advent of new technologies can be overwhelming. Adjusting to them takes time. Now, with the coming of age of AI, teachers, administrators, students, and parents have endless questions and ideas on how it might positively or negatively influence education. I’ve seen it in my current role, in which I continue to empower educators and states to use modern technology to support student learning. And while concerns about AI are valid, there are many positive potential outcomes. For educators in particular, AI can be a huge value-add, automating certain administrative tasks, helping understand and predict student success and struggles, and even helping tailor instruction for individual students.

The upside is huge. As schools embark on their AI journeys, it’s important to remember that we’ve been here before–from the introduction of the internet in classrooms to the abrupt shift to e-learning at the outset of COVID-19. Superintendents, boards of education, and other education leaders can draw on important lessons from prior technological transformations to fully take advantage of this one.

Here are some rules of the road for navigating the integration of disruptive technologies:

1. Choose the right tools. The AI tool(s) you choose can have varying results. School districts should prioritize proven technologies with a track record in education. For students, this includes adaptive learning platforms or virtual tutors. Some of the best tools are those that are specifically designed by and for educators to expedite administrative tasks such as grading and lesson planning. Even more valuable is the ability to support education-specific issues such as identifying struggling students with early warning systems and using AI to provide projections for student futures.
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August 8, 2025
Reimagining the Flipped Classroom: Integrating AI, Microlearning, and Learning Analytics to Elevate Student Engagement and Critical Thinking – Faculty Focus

The traditional flipped classroom model, which reverses the conventional lecture-homework structure, has evolved significantly since its inception. In the digital era, especially post-pandemic, this strategy has gained renewed importance for fostering active learning, critical thinking, and academic resilience. Today, the flipped classroom is no longer just about moving lectures online but about curating immersive, personalized learning environments. The integration of Artificial Intelligence (AI), microlearning, and learning analytics provides new dimensions to this pedagogical approach, enhancing both student preparation and in-class engagement. This article explores how these three components can reshape flipped learning and address persistent challenges such as student unpreparedness, fear of participation, and lack of motivation.

Flipped Classroom 2.0: Enriching the Core Strategy

The original flipped classroom model aimed to reverse the traditional structure of education by shifting the transmission of basic content, often done through lectures, outside the classroom, while using face-to-face class time for active engagement and problem-solving. While this model offered significant benefits, the ever-changing dynamics of education in the digital era demand a more adaptive and responsive framework. This has led to the evolution of Flipped Classroom 2.0, a pedagogy that not only retains the foundational principles of the original model but enhances them with modern technologies and learner-centered practices.

AI Tools

In the Flipped Classroom 2.0 model, the learning process is amplified by integrating Artificial Intelligence (AI) tools, which help personalize student learning pathways. AI-enabled systems such as adaptive learning platforms, chatbots, and generative tools like ChatGPT can provide students with on-demand explanations, simulate peer discussions, offer low-stakes quizzes, and give instant feedback. This technological support allows students to learn at their own pace and revisit complex material without feeling anxious or dependent solely on the instructor. From the educator’s side, AI tools help identify patterns in learner behavior and performance, allowing for more targeted interventions during class time.

Microlearning

Microlearning plays a central role in supporting pre-class preparation under the Flipped Classroom 2.0 framework. Instead of overwhelming students with lengthy video lectures or dense readings, microlearning delivers short, focused modules that concentrate on single learning objectives. These modules are ideal for students who are accustomed to fast-paced digital environments and may have limited attention spans. Microlearning enhances retention by segmenting information and reinforcing key concepts through repetition and brief assessments. It also empowers students to learn efficiently on mobile devices and during flexible hours, which is particularly valuable in hybrid and online learning contexts.

Learning Analytics

Learning analytics represents another foundational component of the Flipped Classroom 2.0 design. By utilizing Learning Management Systems (LMS) and built-in analytics dashboards, instructors can gain valuable insights into which students have completed pre-class activities, how they have performed, and where they may need additional support. Analytics tools also promote student accountability, as learners are able to track their own progress and receive early alerts when they are falling behind. These data-driven approaches make it possible to transition from reactive teaching to proactive guidance.

Collectively, these innovations: AI, microlearning, and learning analytics, enhance the effectiveness of the flipped classroom by aligning content delivery, student support, and real-time feedback. Flipped Classroom 2.0 is not merely a technological upgrade; it is a reimagined learning experience that promotes self-directed learning, critical thinking, and inclusivity. It encourages students to become active participants rather than passive recipients, and it positions educators as facilitators and data-informed decision-makers in the learning process.

Understanding Student Resistance in the Digital Era

Unpreparedness often stems not from laziness or disinterest, but from deeper psychological or contextual barriers that influence a student’s ability to engage. In many cases, students may feel overwhelmed by the unfamiliarity of new instructional models or lack the confidence needed to navigate them successfully. This resistance is especially prominent in environments where flipped classroom strategies are introduced abruptly or without adequate scaffolding. In digitally enhanced learning spaces, Artificial Intelligence tools can serve as diagnostic aids to help educators understand the roots of student resistance.

By monitoring metrics such as time spent on tasks, levels of participation, and types of interactions within pre-class activities, AI systems can identify patterns of disengagement or confusion. For example, if students frequently abandon certain video modules or quiz sections, this may signal content complexity, misalignment with learning styles, or underlying cognitive fatigue. Moreover, many students, especially those transitioning from traditional learning environments, may carry negative assumptions about active learning approaches. They may perceive them as chaotic, unpredictable, or more demanding than passive learning formats. Educators must dig deeper to explore these sentiments and confront the emotional undercurrents that influence classroom behavior. Addressing resistance with empathy, transparency, and gradual acclimatization is crucial for long-term student buy-in.

Addressing Fear Through Technology-Supported Safety

Fear is one of the most formidable barriers to classroom participation. Whether it’s the fear of giving incorrect answers, speaking publicly, or being judged by peers, these emotions can significantly hinder student engagement. In the flipped classroom, where much of the responsibility for learning shifts to students, this fear may become even more pronounced if not properly addressed. Technology, particularly AI-driven tools, can be strategically used to create psychologically safe learning environments. For example, private practice environments powered by chatbots or simulations can offer students a space to make mistakes, ask questions, and rehearse ideas without fear of embarrassment. These tools are particularly beneficial for introverted students or those who lack confidence in their subject knowledge.

Microlearning also plays a vital role in reducing anxiety. By breaking down complex content into manageable chunks, students can focus on mastering one concept at a time. This incremental mastery builds confidence and reduces cognitive overload. When paired with formative assessments and feedback loops, microlearning helps normalize error-making as part of the learning process. Analytics further enhance psychological safety by providing both educators and students with objective data on progress. When students see that others are also struggling with the same concepts or that their performance is gradually improving, they are more likely to perceive learning as a collective and iterative journey.

Making Learning Visible with Analytics and Scaffolds

Visibility of learning progress is essential in maintaining student accountability and fostering meaningful classroom interactions. In traditional flipped classrooms, instructors often face the challenge of determining whether students have genuinely completed pre-class tasks or engaged with the material in a substantive way.

Flipped Classroom 2.0 addresses this issue by leveraging data and scaffolding techniques that make learning both measurable and visible. Learning analytics tools embedded in LMS platforms can track completion rates, quiz scores, engagement time, and areas of difficulty. This data is accessible to both teachers and students through visual dashboards, which serve as powerful motivators and intervention tools. Educators can use this information to adjust instruction, form differentiated groups and provide individualized support. Scaffolding further supports this visibility. Templates, guided note-taking structures, interactive mind maps, and learning journals help students organize their thoughts and reflect on their understanding. When integrated with AI, these scaffolds can be customized based on student performance and learning preferences. This transparency not only improves learning outcomes but also enhances communication between instructors and students.

Designing Pre-Class Work That Drives In-Class Success

A key feature of the flipped classroom model is the dependency on students completing pre-class work so they can actively participate in class discussions and activities. However, the perceived value of pre-class tasks directly influences student compliance and effort. To elevate this value, educators must clearly connect pre-class preparation with in-class success. AI can support this connection by generating customized content summaries, quizzes, and diagnostic questions based on each student’s pre-class performance. For instance, if a student demonstrates mastery of foundational concepts, they can be prompted with higher-order questions to challenge their thinking. Conversely, students who struggle can be provided with remedial resources and practice modules. In-class activities should build directly on pre-class efforts. Group discussions, simulations, debates, and problem-solving exercises should reference or utilize outputs from the pre-class assignments. This integration reinforces the message that preparation is essential for success and ensures that students recognize the practical benefits of investing time in asynchronous learning.

Gamification techniques, such as digital badges, progress milestones, and peer recognition can further motivate students to complete pre-class tasks. When these activities contribute to course grades or unlock exclusive learning opportunities, their relevance is amplified.

Establishing Routine and Engagement with Focusing Activities

The transition from asynchronous pre-class learning to synchronous in-class application can be challenging if not facilitated through structured routines. Focusing activities, particularly those conducted at the beginning of class, serve as critical bridges that help students activate prior knowledge and reorient their attention. These activities may include short reflective prompts, polls, problem-solving warmups, or collaborative brainstorming sessions. When enhanced with technology, focusing activities become even more impactful. For example, AI-driven polling platforms can pose adaptive questions tailored to the students’ recent performance.

Visualization tools can help students construct or revise concept maps based on their understanding of the pre-class content. Establishing predictable routines around these activities builds habits of participation. Over time, students internalize the rhythm of flipped learning: prepare independently, apply collaboratively, reflect continuously. Such engagement patterns increase cognitive readiness and reduce classroom management challenges.

Avoiding Pitfalls in Tech-Enhanced Flipped Classrooms

While Flipped Classroom 2.0 offers numerous benefits, its success is contingent upon thoughtful implementation. Several common pitfalls can undermine the model’s effectiveness. One of the most prevalent mistakes is treating technology as a substitute for pedagogy. Simply replacing lectures with videos or adding quizzes does not constitute a true flip. Without alignment to learning objectives and instructional goals, these tools can confuse rather than support learners.

Another challenge is content overload. Students may feel overwhelmed if asked to review long readings, videos, and quizzes without clear guidance or purpose. Microlearning and scaffolding should be used to break down materials and provide learning pathways that are manageable and coherent. Educator readiness is another often-overlooked factor. Successful adoption of AI tools and data dashboards requires faculty training and support. Instructors must be equipped not only with technical skills but also with an understanding of how to interpret analytics and adjust pedagogy accordingly.

Lastly, student feedback should be incorporated into the design and refinement of flipped classroom practices. Continuous evaluation, surveys, and informal check-ins can reveal usability issues and highlight opportunities for improvement.

Toward a Future-Ready Learning Model

Reimagining the flipped classroom requires more than adopting trendy tools, it demands a philosophical shift. By combining AI, microlearning, and learning analytics, educators can create classrooms that are not only flipped but also fluid, feedback-driven, and future-ready. These strategies support student autonomy, foster critical thinking, and build academic resilience in an unpredictable world. The next frontier of flipped learning is not about technology for its own sake, but about purposeful integration that places learners at the center.

N. P. K. Ekanayake

Ms. Neranjana Priyangani Kumari Ekanayake, a senior lecturer at the University of Kelaniya, teaches Investment and Portfolio Management, Enterprise Resource Planning, Behavioural Finance, and Advanced Management Accounting. She holds a BBA in Accounting (Special) and an MSc in MMs. Neranjana Priyangani Kumari Ekanayake, a senior lecturer at the University of Kelaniya, teaches Investment and Portfolio Management, Enterprise Resource Planning, Behavioural Finance, and Advanced Management Accounting. She holds a BBA in Accounting (Special) first-class and an MSc in Management (Specialized in Finance) and is a CIMA passed finalist. Further she has completed a Diploma in Enterprise Resource Planning in University of Kelaniya.

N.K.L. Silva

Ms. Nilanthige Kaushalya Lakmali Silva is a Lecturer at the Department of Accountancy, University of Kelaniya. She teaches Management, Economics, Human Resource Management, Marketing Management, Strategic Management and Information Management. Ms. Silva holds a B.B.Mgt. (Special) Degree in Accountancy with a First-Class Honours and an M.Sc. in Management with a Merit. Her research interests include major areas of Accounting and Finance, with a particular focus on Sri Lanka’s Capital Markets, Micro and Macro Economic Environment, Corporate Governance, and Corporate Fraud. She has published several research papers in academic journals and conference proceedings. She is a CMA Sri Lanka Passed Finalist and has completed the Diploma in Banking and Finance at the Institute of Bankers of Sri Lanka.

References

Berrett, D. (2012, February 19). How “flipping” the classroom can improve the traditional lecture. Chronicle of Higher Education. https://www.chronicle.com/article/how-flipping-the-classroom-can-improve-the-traditional-lecture/130857

Gerstein, J. (2012, May 15). Flipped classroom: The full picture for higher education. User Generated Education. https://usergeneratededucation.wordpress.com/2012/05/15/flipped-classroom-the-full-picture-for-higher-education/

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July 2, 2025
Integrating Systems Thinking to Enhance Liberal Arts Curriculum through Learner-Centered Teaching – Faculty Focus
Liberal arts education empowers individuals to become well-rounded to handle complexity, diversity, and change by providing broad knowledge of the world and in-depth study in a specific area. It fosters social responsibility, strong intellectual and practical skills, and the ability to think critically to apply knowledge in real-world settings, combining general education with specialized study (Barker, 2000). Today’s modern liberal education typically encompasses a general education curriculum that offers extensive learning across various multiple disciplines and perspectives, paired with focused study in a chosen major.

Liberal arts education has long been valued for its broad-based approach to learning, fostering critical thinking, creativity, and a well-rounded knowledge base. However, in an increasingly complex and interconnected world, there is a growing need to enhance this traditional model with innovative teaching methods (Barker, 2000). Systems thinking and learning-centered teaching are two such approaches that, when integrated, can significantly enrich the liberal arts educational experience (Forrester, 2016). Key learning outcomes from a Liberal Arts Curriculum include:
- A liberal arts curriculum fosters a comprehensive understanding of human cultures and the physical and natural world through diverse fields such as sciences, mathematics, social sciences, humanities, histories, languages, and the arts. This knowledge is deepened by engaging with significant, both contemporary and enduring, real-world problems.
- Additionally, it cultivates intellectual and practical skills, including inquiry and analysis, critical and creative thinking, written and oral communication, quantitative literacy, information literacy, teamwork, and problem-solving. These skills are honed extensively across the curriculum, within the context of increasingly challenging problems, projects, and performance standards.
Systems Thinking Principles and Framework for Liberal Arts Curriculum and Interdisciplinary Learning

Systems thinking is a powerful tool for promoting students’ holistic thinking, interdisciplinary learning, critical and creative thinking skills for problem solving (Meadows, 2008; Richmond, 1994, 2000). Systems thinking complements interdisciplinary learning. For example, environmental science applies systems thinking to study ecosystems and environmental sustainability, integrating ecological, economic, and social dimensions to address environmental challenges holistically. Economics utilizes systemic relationships and feedback loops in its models to analyse market dynamics, resource allocation, and the impact of economic policies on various sectors. Sociology examines social systems through their structures, functions, and interactions, using systems thinking to understand societal issues like inequality, social change, and community dynamics. This synergy between systems thinking and liberal arts curriculum enhances students’ learning and ability to:
1. Identify Patterns and Relationships: Systems thinking helps students recognize patterns and relationships across disciplines, fostering a deeper understanding of how various factors interact within a system.
1. Develop Holistic Solutions: By considering the broader context, students can develop more holistic and sustainable solutions to complex problems.
1. Enhance Critical Thinking: Systems thinking encourages critical analysis and the ability to see beyond surface-level issues, promoting a more nuanced and informed approach to problem-solving.
For this it is essential to introduce the concepts and principles of systems thinking (Figure 1). It is essential for students to learn a variety of systems thinking concepts and tools such as plotting dynamic behaviour patterns (behaviour over-time graphs, BOTGs), causal loops diagrams (CLDs) with reinforcing (R) and balancing (B) feedback loops, system archetypes for constructing models (see for example, Meadows, 2008; Richmond, 1994, 2000; Sterman, 2000).

Figure 1. Tools of systems thinking

Applying these systems thinking tools and concepts students will be able to make connections between disciplines to achieve the intended learning outcomes through integrated learning and problem-solving skills.

Purpose and System/Problem Boundary Identification
- Purpose: In liberal arts, the purpose is to develop well-rounded individuals with critical thinking, problem-solving, and ethical reasoning skills. Systems thinking helps students understand the broader purpose of their education and how different disciplines interconnect.
- System or Problem Boundary Identification: This involves defining what is included within the system and what is excluded. In liberal arts, this helps students delineate the scope of their studies and understand the limits of their knowledge.
Causation and Feedback Loops
- Causation: Understanding cause-and-effect relationships is crucial in liberal arts. Systems thinking helps students analyze how different factors influence each other within a system.
- Feedback loops: This involves recognizing how outputs of a system can loop back as inputs, influencing future behavior. In liberal arts, this principle helps students understand dynamic interactions and long-term consequences of actions.
Validity Criteria and Experiment Design for Validity
- Validity Criteria: Establishing criteria to assess the validity of information and arguments is essential in liberal arts. Systems thinking provides tools to evaluate the reliability and relevance of data.
- Experiment Design for Validity: Designing experiments or studies to test hypotheses and validate theories is a key skill. Systems thinking helps students create robust experimental designs to identify the policy levers that account for complex variables.
Policy Space and Experiment Design for Policy
- Policy Space: This involves exploring different policy options and their potential impacts. In liberal arts, students learn to consider various perspectives and the broader implications of policies.
- Experiment Design for Policy: Designing experiments to test policy interventions helps students understand the practical applications of their studies. Systems thinking aids in creating comprehensive policy experiments that consider multiple factors.
Integration of Systems Approach and Learner-Centered Teaching

Learner-centered teaching, as outlined by Weimer (Weimer 2010, 2013), fundamentally transforms the traditional classroom into dynamic, placing students at the heart of their educational journey. This approach not only involves students in the intricate process of learning but also empowers them to take charge of their educational experiences. By fostering reflection, collaboration, and explicit skill development, learner-centered teaching equips students with critical thinking and problem-solving skills essential for lifelong learning and success. When this approach is harmoniously integrated with essential pedagogical actions for engaging in activities that require independent work or collaboration, students have the chance to discover their preferred learning methods, solve problems, and explore concepts on their own or with peers. Thus, this holistic approach empowers learners to become active, self-directed participants in their educational journey, preparing them for the challenges of the modern world (Blumberg, 2019).

Liberal arts education faces several challenges, including the need to stay relevant in a rapidly changing world and to equip students with skills that are applicable across various fields. Integrating systems thinking and learning-centered teaching can address these challenges by making education more dynamic, interactive, and applicable to real-life situations (Forrester, 2016). For example, a history course might use systems thinking to analyze historical events within broader social, economic, and political contexts, while employing learning-centered techniques to encourage students to draw parallels with contemporary issues. Integrating systems thinking into liberal arts education can be achieved through various learner-centered strategies:
1. Interdisciplinary Courses: Designing courses that explicitly incorporate systems thinking and draw on multiple disciplines can help students see the connections between different fields of study.
1. Project-Based Learning: Encouraging students to work on projects that require interdisciplinary collaboration and systems thinking can enhance their problem-solving skills and real-world application of knowledge.
1. Collaborative Learning Environments: Creating learning environments that promote collaboration and dialogue among students from diverse academic backgrounds can foster a culture of interdisciplinary learning.
In conclusion, teaching in a liberal arts classroom, learner-centered environments empower students to build their own understanding by drawing on their prior knowledge, beliefs, and cultural practices. By incorporating systems thinking into the design and implementation of liberal arts curriculum and interdisciplinary course content, institutions can further enhance students’ holistic thinking and creative problem-solving skills.

Dr. Bellam Sreenivasulu earned his Ph.D. in Chemistry from the National University of Singapore. He has taught at NUS, SUTD, and MDIS, and co-taught with MIT faculty at SUTD in Singapore. Currently, he is a Senior Lecturer at Residential College 4, NUS. He specializes in Systems Thinking and System Dynamics (ST&SD) education, focusing on energy systems, sustainable energy production, energy supply and demand, energy security, and carbon emissions. He also teaches a course on sleep health using systems approach. Passionate about interdisciplinary and learner-centered teaching and research.

References

Blumberg, P. (2019). Making learning-centered teaching work: Practical strategies for implementation (2nd ed.) Stylus.

Barker, C.M. (2000). Liberal Arts Education for a Global Society. New York: Carnegie Corporation of New York.

Forrester, J. W. (2016). Learning through System Dynamics as Preparation for the 21st Century. System Dynamics Review, 32, 187−203

Meadows, D. (2008). Thinking in Systems: A Primer (Chelsea Green Publishing, Hartford, VT).

Richmond, B. (2000). The “Thinking” in Systems Thinking: Seven Essential Skills (Pegasus Communications, Waltham, MA).

Weimer, M. (2010). Inspired college teaching. San Francisco, CA: Jossey-Bass.

Weimer, M. (2013). Learner-centered teaching: Five key changes to practice (2nd ed.). San Francisco, CA: Jossey Bass.
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June 23, 2025