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Ethical Considerations Regarding the Use of AI in Higher Education

Ethical Considerations Regarding the Use of AI in Higher Education

Jan Spruijt

Artificial Intelligence (AI) represents a remarkable advancement in technology with the potential to significantly benefit higher education. AI tools, such as ChatGPT, can support educators and enhance student learning experiences by automating administrative tasks, providing personalized learning pathways, and facilitating access to a vast array of information. However, while the benefits of AI are numerous, there is increasing concern within academic circles about its ethical implications and the challenges it presents to traditional educational paradigms in business and engineering education.

If you’re a college student preparing for life in an A.I. world, you need to ask yourself: Which classes will give me the skills that machines will not replicate, making me more distinctly human? New York Times

Recent studies, including the work by Sullivan, Kelly, and Mclaughlan (2023), highlight these concerns in detail. Their research underscores significant issues related to academic integrity and student learning outcomes with the advent of generative AI tools like ChatGPT. They note that while these tools can aid learning, they also present substantial risks, particularly in terms of undermining the learning process and compromising the integrity of academic work.

Key Concerns Raised

The integration of AI tools like ChatGPT into higher education has brought about several significant concerns, primarily revolving around academic integrity, the quality of student learning, and broader ethical implications.

1. Academic Integrity Concerns:

One of the most pressing issues is the potential for AI to facilitate academic dishonesty. AI tools can generate essays, solve problems, and create presentations, often indistinguishable from student-generated work. This ease of use can lead students to rely on AI to complete their assignments, effectively bypassing the learning process. According to Sullivan, Kelly, and Mclaughlan (2023), there has been a noticeable increase in students submitting work produced by ChatGPT, yet these students struggle with verbal discussions or critical analyses of the same topics. This phenomenon undermines the authenticity of academic assessments and devalues educational qualifications.

2. Impact on Learning Outcomes:

AI’s ability to generate content quickly and accurately poses a threat to the learning process itself. As noted in Sullivan et al.’s study, there is a critical link between the act of writing and learning (Goodman, 2023). The cognitive effort involved in structuring an argument, synthesizing information, and reflecting on content is essential for deep learning. When students use AI to circumvent these processes, they miss out on the development of critical thinking and analytical skills. The concern here is that AI can make learning too easy, removing the struggle that is often necessary for true understanding and mastery of complex subjects (AI writing tools garner concern about academic integrity, education from faculty, 2023).

“The potential benefits of artificial intelligence are huge, so are the dangers.” (Dave Waters)

3. Quality and Reliability of AI-Generated Content:

Another significant concern is the quality and reliability of the output generated by AI tools. ChatGPT and similar AI systems can produce text that appears plausible but can contain factual inaccuracies, logical fallacies, and a lack of nuanced understanding (Chatbots ‘spell end to lessons at home’, 2023). Additionally, AI lacks the ability to form genuine opinions, think creatively, or critically evaluate its own outputs. These limitations can lead to students presenting erroneous information and can hinder the development of their critical evaluation skills.

4. Equity and Accessibility Issues:

The ethical implications of AI use extend to equity and accessibility. Not all students have equal access to advanced AI tools, potentially exacerbating existing inequalities in education. Students from disadvantaged backgrounds may not be able to afford the technology or may lack the necessary digital literacy to use it effectively. This creates a divide where some students can leverage AI to enhance their academic performance, while others are left behind.

5. Data Privacy and Security:

AI tools often require access to vast amounts of data, raising concerns about the privacy and security of student information. There is a risk that sensitive data could be mishandled or exposed, leading to potential breaches of privacy and trust (The Promise and Peril of ChatGPT in Higher Education, Park & Ahn, 2024).

6. Reduced Critical Thinking and Engagement:

The use of AI can lead to a passive learning experience where students become overly reliant on technology. This reliance can diminish opportunities for active learning, critical thinking, and problem-solving – skills that are crucial for academic and professional success. According to Rasul et al. (2024), the passive nature of AI-assisted learning contradicts the principles of constructivist learning theory, which emphasizes the importance of active engagement and social interactions in the learning process.

7. Inadequate Assessment of Learning Outcomes:

AI-generated content makes it difficult for educators to assess students’ true understanding and skills accurately. Traditional assessment methods, such as essays and take-home assignments, are easily completed with AI assistance, making it challenging to gauge a student’s actual learning progress. This issue calls for a reevaluation of assessment strategies to ensure they effectively measure student learning outcomes in an AI-enhanced educational environment.

8. Technological and Psychological Challenges:

Technological solutions to detect AI-generated content are still evolving and are not foolproof. False positives can lead to unwarranted accusations of academic dishonesty, causing psychological stress and potentially damaging student reputations (The Promise and Peril of ChatGPT in Higher Education, Park & Ahn, 2024). Additionally, as AI technology advances, detection systems must continuously adapt, posing a persistent challenge for educational institutions.

I believe that the future of AI in education encompasses three essential areas: AI literacy, AI policy, and AI misconduct, or ’AI-giarism.” Professor Chan

Given these multifaceted challenges, it is imperative to explore both traditional and innovative strategies to mitigate the negative impacts of AI while harnessing its benefits.

Routes for Addressing These Challenges

Old-School Methods

I’m not a fan of these methods, but lecturers are increasingly holding on to them in the age of AI. They’re effective, but often very time-consuming and increasingly less effective as AI continuous to become more accessible.

  1. Reinforcing Traditional Assessments: Maintaining some traditional forms of assessment, such as invigilated exams, can help ensure that students engage with the material directly. However, reliance solely on these methods can stifle innovation and adaptability in education.
  2. Handwritten Assignments: Encouraging handwritten work can reduce the opportunity for AI misuse. Yet, this approach may not fully capture the interactive and digital nature of modern education.

New-School Methods

  1. AI-Resistant Assessments: Developing assessment types that are difficult for AI to handle is a promising approach. This includes oral presentations, podcasts, laboratory activities, group projects, and specific assignment prompts that require critical thinking and creativity beyond the capabilities of current AI.
  2. Game-Based Learning: Research by Sánchez-Ruiz et al. (2023) shows that serious games and simulation for higher education are hard for AI to effectively participate in due to the complexity of understanding game algorithms and dynamic scenarios. These games not only make learning engaging but also ensure that students actively apply their knowledge and skills in a way that AI cannot replicate.
  3. Flipped Classroom Models: This approach involves students engaging with lecture material at home through digital platforms and using classroom time for interactive, hands-on activities. Research by Sánchez-Ruiz et al. (2023) shows that flipped classrooms and other blended learning methodologies significantly reduce the effectiveness of AI tools in completing assignments, as these require active participation and critical engagement from students.
  4. Integrated AI Use: Embracing AI as a tool within the learning process rather than banning it can also be beneficial. By teaching students how to use AI responsibly and integrating it into the curriculum, educators can prepare students for a future where AI will be an integral part of their professional lives. This includes developing digital literacy and critical evaluation skills to assess AI-generated content.

“The learners are in charge, the AI is there to work with the educators to support students to be the best learner they can be. Human educators are a much sought-after resource in this vision, because everybody will need to learn throughout their lives.” Rose Luckin

Conclusion

The introduction of AI in higher education is a double-edged sword, offering both significant advantages and substantial challenges. While traditional methods can provide short-term solutions to maintain academic integrity, it is the innovative, new-school approaches that hold the potential to transform education sustainably. By incorporating AI-resistant assessment methods and integrating AI into the learning process responsibly, educators can enhance student learning while preserving the integrity and value of higher education. As we navigate this evolving landscape, it is crucial to engage in ongoing dialogue and adapt our strategies to ensure that the use of AI in education aligns with our core educational values and goals.

Chris Freeman: the entrepreneur who created the concept of ‘innovation studies’

Jan Spruijt

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After our deepdive into the story of Ted Levitt last week, we’ll dive into the extraordinary story of Christopher Freeman (1921 – 2010) this week. By many renowned for his substantial conributions to the academic field of innovation, and as such named a ‘social sciences entrepreneur’ (Fagerberg, Fosaas, Bell and Martin, 2011), Chris Freeman was an expert at the intersection of economics and innovation. His legacy included many ‘firsts’, such as being the first director of SPRU – the Science Policy Research Unit at the University of Sussex -, the first Frascati manual (1962), the first studying ‘technology gaps’ (1960s), the first editor of Research Policy and the author of the first textbook on innovation, ‘The Economics of Industrial Innovation’. His contributions to theory also included works on Industrial Waves and Disruption, National Innovation Systems and The Economics of Change. Freeman had a significant contribution to schumpetarian thinking about disruption.

Christopher Freeman (Fagerberg et al, 2010)

Background

Christopher Freeman was born in Sheffield in the United Kingdom. He was raised with liberal ideas: his father Arnold worked for the Webb’s whose ‘Fabian Society’ shaped reformist-socialist left-wing views on democracy. His father later on pioneered with ‘Workers’ Educational Association’ and as such also heavily impacted his son’s radical ideas. Christopher Freeman “was educated at the progressive Abbotsholme School in Staffordshire, where he founded a cell of the Young Communist League” (Independent, 2010). He started studying at the London School of Economics (LSE) in 1941, continued in Cambridge to study economics, which he interrupted to join the Royal Military Academy, after which he returned to LSE to graduate in 1948. At LSE he was part of an extraordinary cohort, which included Norman MacKenzie, Claus and Mary Moser and Marion Stern, the mother of Nicholas Stern, an economist and well-known ambassador of taking early action on climate change. Nicholas later said that his passion was largely due to the discussions that he had been part of when his parents, Freeman and other classmates in his early childhood (Stern, 2017). This also forms the main line in Freeman’s career: on the one hand he followed the line of thinking of Schumpeter, popularizing Schumpeter’s – now widely accepted – idea of creative destruction and the fact that there no boundaries to growth, and on the other hand a critical acclaim to capitalism, claiming that a more institutional approach to innovation systems – an approach that is now classified as neo-schumpeterian economics, which takes ingredients from different economic schools such as the austrian school, lausanne school and keynesiasism.


His ideas and early career expertise on innovation policy, led to one of his most famous contributions to the academic world: the notion of innovation systems, and later on also his work on national systems of innovation (Guardian, 2010). He also argued that science plays an important role in establishing industries, much in line with Bernal’s work on the science-based industry and science policies (Fagerberg et al, 2010). As such, he is widely recognized for being ‘an entrepreneur’ in science, developing innovation science as a separate field of study alongside economics, management and entrepreneurship – which led to him being the first director of SPRU – the Science Policy Research Unit at the University of Sussex. As an academic he supervised many scholars, wrote many articles and books and as such had a lasting impact on the science, economics and industry of innovation.

Key Ideas

  • The Economics of Industrial Innovation: Freeman adopted a dynamic perspective on capitalism, heavily influenced by Marx and Schumpeter. He viewed capitalism as a system characterized by continuous interaction between technological progress, capital accumulation, and social and institutional conditions. Drawing from that, Freeman emphasized the role of technological competition between firms as a driving force behind capitalist development. He focused on understanding the dynamics of technological change within firms and its implications for economic growth and competitiveness. Freeman argued that traditional economic theories often failed to adequately account for the role of research and development (R&D) in driving technological progress and economic development. He highlighted the importance of R&D activities within firms and advocated for policies and management strategies that promote innovation.
  • National Innovation Systems: Freeman, together with i.e. Lundvall, was instrumental in developing the concept of national innovation systems, which emphasizes the interconnectedness of various actors, institutions, and policies within a country that contribute to innovation and economic development. He advocated for a holistic approach to understanding innovation processes, beyond the traditional focus on individual firms or sectors. He emphasized the role of institutions, including government policies, research organizations, universities, and industry associations, in shaping national innovation systems. He argued that effective innovation policies require a deep understanding of the institutional framework within which innovation occurs (Freeman, 1995). In his own words:

“Sectoral systems, regional systems are all very constructive and helpful. (…) You can point to the success of Pakistan in medical instruments or Brazil in boots or shoes or whatever (…) and if you point out the role of innovation in all those micro level studies, that’s very useful. And if you point out certain regions of countries are more innovative, and the north of Italy has contributed more to the growth of the country than Sicily, that’s all very useful. But I don’t think you’ll change the main paradigm of neoclassical economics, I think you have to attack it head on in the centre (…) Most of the people working on innovation systems prefer to work at the micro-level. They are a bit frightened still of the strength of the neoclassical paradigm at the macroeconomic level. But I think that’s where they have to work. You have to have an attack on the central core of macroeconomic theory. It is happening but not happening enough.” (Unpublished interview with Sharif, 2003)

Impact

  • Public impact: Freeman highlighted the importance of knowledge diffusion and learning mechanisms within national innovation systems. He emphasized the role of networks, collaborations, and knowledge spillovers in facilitating innovation and technology transfer. Freeman’s work on national innovation systems had significant policy implications. He advocated for policies that promote collaboration between government, industry, and academia, as well as investments in education, research infrastructure, and technology transfer mechanisms. His ideas contributed to the design of innovation policies aimed at enhancing the competitiveness and resilience of national economies (Fagerberg et al, 2011).
  • Industrial impact: Freeman emphasized the importance of fostering a culture of innovation within industries. He highlighted the role of research and development (R&D) activities, technological competition, and dynamic interactions between firms in driving innovation. By advocating for policies and management strategies that support innovation, Freeman encouraged industry leaders to prioritize investments in R&D, technology development, and knowledge creation.
  • Academic Impact: Under his leadership, SPRU became a global center for innovation research, attracting scholars, policymakers, and industry practitioners from around the world. This collaborative environment facilitated knowledge exchange, interdisciplinary research, and the co-creation of innovative solutions to industry challenges.

Key take-aways

  1. Cross-Disciplinary Collaboration: Embrace collaborative efforts across disciplines to gain comprehensive insights into innovation dynamics.
  2. Emphasize a Systems Approach: Consider the historical, economic, and systemic dimensions of innovation to develop a nuanced understanding of its complexities.
  3. Tap into the Academic Community: Foster an inclusive academic environment that encourages knowledge exchange and inspires new avenues of inquiry in innovation studies.
  4. Explore Macro-Level Analysis: Delve into macroeconomic perspectives to discern broader implications of innovation on national and regional innovation ecosystems.
Unleashing Innovation: Strategic Simulation Games as a solution to the use of AI and ChatGPT in Business Education

Unleashing Innovation: Strategic Simulation Games as a solution to the use of AI and ChatGPT in Business Education

Alwin Maes

In the world of business education, the usage of AI and ChatGPT has severe impact. Not only does it open doors for new ways of learning, it also threatens traditional learning methods, activities and deliverables – and forces educators to update curricula in a fast pace. As became apparent over the last few years, strategic simulation games are immersive learning experiences that go hand-in-hand with the rise of the usage AI and ChatGPT in education. As a premium partner of Innovative Dutch, who is developing and running strategic simulation games in universities worldwide, we’ve experienced during the last year that there are many benefits arising from using strategic simulation games in education, knowing that students will move to AI to ask for help. Interested in learning more about the simulation games of Innovative Dutch? Check their website for the Innovation Management Game and the Business Model Game. Their games are played in over 25 countries by 10K+ students annually.

Here are nine advantages of leveraging strategic simulation games enhanced by AI in business education:

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Avoid the Toxic Trap: the Toxic Matrix

Avoid the Toxic Trap: the Toxic Matrix

Jan Spruijt

Innovation is the foundation of progress and success in business. A culture of innovation is essential for companies to thrive and stay competitive. One of the biggest challenges for companies is to ensure that the culture of innovation is not overshadowed by a culture of toxicity. There are a number of potential pitfalls that must be avoided in order to create an environment that encourages creativity and collaboration. This blog will discuss these issues, as well as strategies for avoiding the Toxic Trap.

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Business Model Innovation Canvas

How to blend 10 Types of Innovation with the Business Model Canvas?

Jan Spruijt

Simple but effective: I’ve tried to combine the excellent framework of 10 Types of Innovation (Keeley et al, 2013) with the highly successful framework of the Business Model Canvas (Osterwalder, Pigneur et al, 2008). I wasn’t the first one to come up with this idea, some others have plotted the 10 types on the BMC before, such as Huw Griffiths on Medium or Heather McQuaid on Slideshare.

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Ambidextruous Organization

The Ambidextrous Organization

Jan Spruijt

4 Paths to a Sophisticated Innovation Strategy

In december I reached out to both Alexander Osterwalder and John Bessant and asked them what is the most important organizational skill for engaging continuously with innovation. Their answers were almost the same:

  • Osterwalder mentioned that every board should consist of both a Chief Executive Office and a Chief Entrepreneurship Officer.
  • Bessant noted that organizations should always find a balance between innovators and innovation managers.

Shorty after, I read an article by Ayse Birsel, on Inc.com1. She also talked to asked Alexander Osterwalder and asked him the question why ‘designers who are fluent at business strategy’ and ‘business people who are fluent at design’ are so different to each other. He could easily name 8 differences between the two of them, but the article concluded with the statement that organizations are in need of both explorers and exploiters – or evolutionaries and revolutionaires2.

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university2040

The University in 2040: 6 trends & an infographic.

Jan Spruijt

On November 23, I had the honor of giving a talk at the NRC Live event for Education. I was scheduled immediately after Bert van der Zwaan, rector magnificus at the University of Utrecht. Van der Zwaan launched his book that day: the result of sabbatical he and his wife took in 2015. During that sabbatical they traveled the world and tried to speak with as many educational visionaries as possible. It led to the work: The University in 2040, does it still exist?

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The Lean Scale-Up: Innovation & Entrepreneurship for New Ventures

The Lean Scale-Up: Innovation & Entrepreneurship for New Ventures

Jan Spruijt

Traditionally, organization design (OD) is an area of expertise focused on the roles and formal structures of organizations. The main goal of OD would be to design the organization in such a way that it makes it possible for the company to reach its vision and thus facilitates the growth.

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The Early Bird Gets the Worm, But the Second Mouse Gets the Cheese: Non-Technological Innovation in Creative Industries

Jan Spruijt

In the most recent edition of the Journal of Creativity and Innovation Management, I ran into an interesting article about being a startup versus being an early adaptor. The article suggests that early adaptors have a higher probability to succeed in the case of non-technological environments than the startups that proceed them.

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Innovation Ecosystem

Schematic overview to understand the complexity of the Innovation Ecosystem (Infographic)

Jan Spruijt

The Innovation Ecosystem

The Innovation Ecosystem is one of the most under-researched topics. One the one hand because policy researchers usually tend to focus more on polls, elections and international collaboration and business researchers usually tend to focus more on organizations and interorganizational collaborations. However, publisher Edward Elgar has repeatedly published interesting works on innovation policy, innovation systems and the like. An ecosystem of innovation could be described as, quoting Wikipedia, the flow of technology and information among people, enterprises and institutions [which] is key to an innovative process. It contains the interaction between actors who are needed in order to turn an idea into a process, product or service on the market. The Innovation Ecosystem is extremely important to the economy and welfare of a country or region. It is one of the main drivers of GDP. Over the past decades more research has been done on the dynamics behind these ecosystems and its subsystems. Below you’ll find a schematic overview of the innovation ecosystem. It will take you to the download side of Innovative Dutch, where you can download it in full resolution.

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