Learning Design
Generative AI Resources Curated by the T&L Commons
Teaching and Learning Commons has curated a LinkedIn Learning resource playlist to support you in your exploration of Generative AI. Prior to engaging in this LinkedIn Learning content, we encourage you to delve into the context of higher education by viewing the following videos.
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Watch the video series AI in a Minute to learn about different AI capabilities and understand the tech behind them. In these one-minute videos, you’ll get simple, easy to understand introductions to basic AI concepts.
General Pedagogy
Carvalho, L., Martinez-Maldonado, R., Tsai, Y., Markauskaite, L., & De Laat, M. (2022). How Can We Design for Learning in an AI World? Computers and Education: Artificial Intelligence, 3, 1-9. https://doi.org/10.1016/j.caeai.2022.100053
Abstract: A critical educational challenge involves figuring out how to support young generations to develop the capabilities that they will need to adapt to, and innovate in, a world with AI. This article argues that both educators and learners should be involved not only in learning but also in co-designing for learning in an AI world. Further, they together should explore the knowledge, goals and actions that could help people shape future AI scenarios, and learn to deal with high degrees of uncertainty. A key contribution of the paper is a re-conceptualization of design for learning in an AI world, which explores a problem space of educational design, and illustrates how educators and learners can work together to re-imagine education futures in an AI world. As part of this problem space, the paper discusses underpinning philosophies (the capability approach and value creation), a high-level pedagogy (with an emphasis on co-creation), pedagogical strategies (speculative pedagogies), and pedagogical tactics (AI scenarios). It then proposes a design framework (ACAD) to support educators and learners’ discussions about design for learning in an AI world. This participatory design approach aims to sensitize people for what education may mean, for whom, and how learning with AI may look like, and it highlights the active engagement of educators and learners in co-designing a future they desire, to help shape learning and living in an AI world.
Fiebrink, R. (2019). Machine Learning Education for Artists, Musicians, and Other Creative Practitioners. ACM Transactions on Computing Education, 19(4). https://doi.org/10.1145/3294008
Abstract: This article aims to lay a foundation for the research and practice of machine learning education for creative practitioners. It begins by arguing that it is important to teach machine learning to creative practitioners and to conduct research about this teaching, drawing on related work in creative machine learning, creative computing education, and machine learning education. It then draws on research about design processes in engineering and creative practice to motivate a set of learning objectives for students who wish to design new creative artifacts with machine learning. The article then draws on education research and knowledge of creative computing practices to propose a set of teaching strategies that can be used to support creative computing students in achieving these objectives. Explanations of these strategies are accompanied by concrete descriptions of how they have been employed to develop new lectures and activities, and to design new experiential learning and scaffolding technologies, for teaching some of the first courses in the world focused on teaching machine learning to creative practitioners. The article subsequently draws on data collected from these courses–an online course as well as undergraduate and masters-level courses taught at a university–to begin to understand how this curriculum supported student learning, to understand learners’ challenges and mistakes, and to inform future teaching and research.
Otsuki, G. J. (2020, January 23). OK computer: To prevent students cheating with AI text-generators, we should bring them into the classroom. The Conversation. https://theconversation.com/ok-computer-to-prevent-students-cheating-with-ai-text-generators-we-should-bring-them-into-the-classroom-129905
Lede: Artificial intelligence-based (AI) programs are quickly improving at writing convincingly on many topics, for virtually no cost. It’s likely in a few years they’ll be churning out C-grade worthy essays for students.
We could try to ban them, but this software is highly accessible. It would be a losing battle.
Long-form writing, especially essay writing, remains one of the best ways to teach critical analysis. Teachers rely on this mode of assessment to gauge students’ understanding of a topic.
Thus, we need to find ways to help students of all disciplines thrive alongside advanced automation and smart systems, rather than resist them. This involves training students to work with AI tools, rather than banning them.
Perrotta, C., & Selwyn, N. (2020). Deep Learning Goes to School: Toward a Relational Understanding of AI in Education. Learning, Media and Technology, 45(3), 251–269. https://doi.org/10.1080/17439884.2020.1686017
Abstract: In Applied AI, or ‘machine learning’, methods such as neural networks are used to train computers to perform tasks without human intervention. In this article, we question the applicability of these methods to education. In particular, we consider a case of recent attempts from data scientists to add AI elements to a handful of online learning environments… we provide a detailed examination of the scholarly work carried out by several data scientists around the use of ‘deep learning’ to predict aspects of educational performance. This approach draws attention to relations between various (problematic) units of analysis: flawed data, partially incomprehensible computational methods, narrow forms of ‘educational’ knowledge baked into the online environments, and a reductionist discourse of data science with evident economic ramifications. These relations can be framed ethnographically as a ‘controversy’ that casts doubts on AI as an objective scientific endeavour, whilst illuminating the confusions, the disagreements and the economic interests that surround its implementations.
Pham, S. T. H., & Sampson, P. M. (2022). The Development of Artificial Intelligence in Education: A Review in Context. Journal of Computer Assisted Learning, 38(5), 1408–1421. https://doi.org/10.1111/jcal.12687
Abstract: In fact, most schools around the world are not well equipped to have discussions and keep current on the expansion of artificial intelligence (AI) in many aspects of society and economy. They either ignore this conversation, or simply criticize technology, but these resistances are not stopping wide spread of various types of AI projects in schools, mainly driven by corporations, and fueled by incentives that might not match well with long term educational objectives of student success, diversity, equity, and inclusivity…The overall purpose is to address the rising gap between the ultrafast development of AI and the meticulous technological application of education, and to suggest the important bridge of building technological leadership in teacher preparation to get ready for the grow of AI in education… The paper provides educators and policy makers an overall background of the phenomenon of AI in education. The study has revealed there is an urgent need for research and development in teacher preparation as well as in the philosophy of technology in education to bridge the gap between AI and education.
Shiohira, K. (2021). Understanding the Impact of Artificial Intelligence on Skills Development. Education 2030. In UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training. UNESCO-UNEVOC International Centre for Technical and Vocational Education and Training. https://eric.ed.gov/?id=612439
Abstract: Artificial intelligence has produced new teaching and learning solutions that are now undergoing testing in different contexts. In addition to its impact on the education sector, AI is substantially altering labour markets, industrial services, agriculture processes, value chains and the organization of workplaces in particular. Technical and vocational education and training (TVET) contributes to sustainable development by fostering employment, decent work and lifelong learning. However, the effectiveness of a TVET system depends on its links and relevance to the labour market. As one of the major drivers of change, there is a need to better understand the impact of AI on labour markets, and consequently on TVET systems. … Regardless of context, all TVET institutions should develop an understanding of the current and future importance of AI and begin to incorporate its use into their planning. Forward thinking and, where possible, pre-emptive action, will put TVET institutions and their graduates in a position to thrive in the era of AI and contribute positively to economic, social, and individual development.
Discipline-specific
Getchell, K. M., Carradini, S., Cardon, P. W., Fleischmann, C., Ma, H., Aritz, J., & Stapp, J. (2022). Artificial Intelligence in Business Communication: The Changing Landscape of Research and Teaching. Business and Professional Communication Quarterly, 85(1), 7–33. https://doi.org/10.1177/23294906221074311
Abstract: The rapid, widespread implementation of artificial intelligence technologies in workplaces has implications for business communication. In this article, the authors describe current capabilities, challenges, and concepts related to the adoption and use of artificial intelligence (AI) technologies in business communication. Understanding the abilities and inabilities of AI technologies is critical to using these technologies ethically. The authors offer a proposed research agenda for researchers in business communication concerning topics of implementation, lexicography and grammar, collaboration, design, trust, bias, managerial concerns, tool assessment, and demographics. The authors conclude with some ideas regarding how to teach about AI in the business communication classroom. [Paper presented at the Annual Meeting of the Association of Business Communication (84th, Detroit, MI, 2019).]
Almelweth, H. (2022). The Effectiveness of a Proposed Strategy for Teaching Geography through Artificial Intelligence Applications in Developing Secondary School Students’ Higher-Order Thinking Skills and Achievement. Pegem Journal of Education and Instruction, 12(3), 169–176. https://eric.ed.gov/?id=EJ1362619
Abstract: The study investigated the effectiveness of employing a proposed strategy for teaching geography using artificial intelligence applications in developing higher-order thinking skills and achievement among secondary school… The study showed significant differences at (0.05) between the mean scores of the students in the control and experimental groups on the achievement test in favor of the experimental group in the posttest. Also, differences were shown between the assessment means of the students in the control and experimental groups on the higher-order thinking skills assessment card in favor of the experimental group. The effect size came high. The study recommends the need to emphasize the expansion of the use of artificial intelligence applications in teaching geography.
Mohamed, M. Z. bin, Hidayat, R., Suhaizi, N. N. binti, Sabri, N. binti M., Mahmud, M. K. H. bin, & Baharuddin, S. N. binti. (2022). Artificial Intelligence in Mathematics Education: A Systematic Literature Review. International Electronic Journal of Mathematics Education, 17(3). https://eric.ed.gov/?id=EJ1357707
Abstract: The advancement of technology like artificial intelligence (AI) provides a chance to help teachers and students solve and improve teaching and learning performances. The goal of this review is to add to the conversation by offering a complete overview of AI in mathematics teaching and learning for students at all levels of education… The analysis revealed that most of the reviewed studies used quantitative research methods. The types of themes for AI in mathematics education were categorized into advantages and disadvantages, conceptual understanding, factors, role, idea suggestion, strategies and effectiveness.
Godwin-Jones, R. (2022). Partnering with Al: Intelligent Writing Assistance and Instructed Language Learning. Language Learning & Technology, 26(2), 5–24.
Abstract: In recent years, advances in artificial intelligence (AI) have led to significantly improved, or in some cases, completely new digital tools for writing. Systems for writing assessment and assistance based on automated writing evaluation (AWE) have been available for some time… In this column, we will be examining AI-enabled writing tools, reviewing the findings from research studies, and discussing their use in instructional settings. When integrated into writing instruction and practice, these digital tools have been found to offer significant benefits to both students and teachers. Teacher mediation aids learners in becoming informed consumers of language technology, as well as helping them to gain meta-linguistic knowledge. For researchers, intelligent writing tool use is optimally analyzed from a broad ecological perspective that examines the dynamic interplay of learner, software, and instructional environment.
Eaton, S.E., Mindzak, M., & Morrison, R. (2021, May 29 – June 3). Artificial Intelligence, Algorithmic Writing & Educational Ethics [Paper presentation]. Canadian Society for the Study of Education Société canadienne pour l’étude de l’éducation, Edmonton, AB, Canada.
Abstract: Paper presented at the Canadian Society for the Study of Education (CSSE) 2021 – Canadian Association for the Study of Educational Administration (CASEA) (June 1, 2021) The purpose of this paper is to provide a theoretical and conceptual discussion of the rapidly emerging field of artificial intelligence (AI) and algorithmic writing (AW). The continued development of new tools—most notably at this time, GPT-3—continues to push forward and against the boundaries between the writing of human and machine. As issues surrounding AI continue to be actively discussed by scientists, futurists and ethicists, educational leaders also find themselves front and centre of debates concerning, academic writing, academic integrity and educational ethics more broadly. Three points of focus provide the basis for this analysis. Firstly, we examine the impact of AW on student writing and academic integrity in schools. Secondly, we discuss similar issues in relation to publication and academic scholarship. Finally, taken together, we discuss the broader ethical dimensions and implications that AI and AW will, and are, quickly bringing into education and the field educational administration and leadership.
Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application of Artificial Intelligence powered digital writing assistant in higher education: Randomized controlled trial. Heliyon, 7(5), e07014. https://doi.org/10.1016/j.heliyon.2021.e07014
Abstract: A major challenge in educational technology integration is to engage students with different affective characteristics. Also, how technology shapes attitude and learning behavior is still lacking. Findings from educational psychology and learning sciences have gained less traction in research. The present study was conducted to examine the efficacy of a group format of an Artificial Intelligence (AI) powered writing tool for English second postgraduate students in the English academic writing context… The results suggest that AI-powered writing tools could be an efficient tool to promote learning behavior and attitudinal technology acceptance through formative feedback and assessment for non-native postgraduate students in English academic writing.
Satar, M. (2021). Speaking with machines: interacting with bots for language teaching and learning. In T. Beaven & F. Rosell-Aguilar (Eds), Innovative language pedagogy report (pp. 133-138). Research-publishing.net. https://doi.org/10.14705/rpnet.2021.50.1248
Abstract: This piece explores technologies for freer communication “with” machines, i.e. bots (chatbots or conversational agents), rather than the concept of speaking “to” machines, such as Intelligent Assistants (IA) like “Alexa”… This chapter describes the use of modern chatbots for language learning, provides examples of how chatbots could be assessed by language learning students and language teachers in training, and discusses the benefits of communicating with computers for language learners and the limitations of conversational agents.
Sumakul, D. T. Y. G., Hamied, F. A., & Sukyadi, D. (2022). Artificial Intelligence in EFL Classrooms: Friend or Foe? LEARN Journal: Language Education and Acquisition Research Network, 15(1), 232–256. https://eric.ed.gov/?id=EJ1336138
Abstract: The disruptive impacts of Artificial Intelligence (AI) are currently affecting various aspects of society, including education. Despite some doubts and fears, many studies suggest that AI could offer advantages to education, and AI-based applications have been developed for teaching and learning, and English as a Foreign Language (EFL) classrooms in particular… this study investigated how teachers perceive the use of AI in their EFL classrooms… The results show that all teachers had positive perceptions towards the use of AI in their classrooms. The teachers agreed that AI could help teachers teach and students learn. Moreover, the interview data also indicates that students’ motivational levels and teachers’ technological and pedagogical competence should be put into consideration when integrating AI into EFL classrooms.
Faculty
Dukewich, K. & Larsen, C. (2023). Working Paper: How Are Faculty Reacting to ChatGPT? [PDF] Curriculum Research Collaborative. Open Forum held on Friday, January 27th from 11:00am to 12:30pm via Zoom
Abstract: Generative AI platforms like ChatGPT have exploded into our cultural awareness this year. Across post-secondary institutions, it was immediately apparent that faculty were eager to explore and discuss what this potentially disruptive technology might mean for them, their courses and their students. We wanted to create an opportunity for that discussion and to get a truer sense of initial faculty reactions than what sensational media headlines were offering. This working paper outlines the results of a facilitated online forum, open to faculty and staff from two institutions in the Lower Mainland of British Columbia in January 2023. Our session invited participants to test ChatGPT, reflecting on its strengths and limitations, and then talk through the potential impacts on instructors, our students, and post-secondary education in general of different approaches: ignore it, fight it, and embrace it. Analysis of participant contributions to polls, group discussions and a highly active chat space provide a snapshot of how faculty and staff were feeling and what they were doing in response to ChatGPT and other generative AI platforms. While the data seems to indicate a relatively optimistic take at this early point in the AI revolution, excerpts from discussions and debates do indicate a range of emotions and reactions–a range that will likely only continue to widen with the continuing release of ever more capable AI.
Muscanell, N. & Robert, J. (2023). EDUCAUSE QuickPoll Results: Did ChatGPT Write This Report? EDUCAUSE. https://er.educause.edu/articles/2023/2/educause-quickpoll-results-did-chatgpt-write-this-report
Abstract: An EDUCAUSE QuickPoll that covers familiarity and disposition of faculty, staff, leadership, and other roles in higher education towards generative AI as well as some common challenges and promising practices.
Library Guides
Tools created in Canada by Contact North
General Strategies
Gray, L. (2023). Reforming Assessment in Higher Education: Leveraging the Opportunity Provided by Language Generation Tools. EdTech Chronicle.
The emergence of language generation tools like ChatGPT in Higher Education has sparked a debate about their impact, particularly on assessment. While some may see these tools as a challenge to conventional teaching methods, it’s important to view them as a chance to improve and transform assessment practices.
Lalonde, C. (2023). ChatGPT and Open Education. BCcampus.
Lede: Last month my BCcampus colleague in Learning + Teaching Gwen Nguyen wrote a blog post about ChatGPT, examining some of the implications of ChatGPT on learning and teaching. I would like to continue looking at ChatGPT and generative AI technologies, but this time through the lens of open education.
Nguyen, G. (2023). Digital Pedagogy Toolbox: Let’s Make Friends with ChatGPT. BCcampus.
Lede: In this sixth post in [the BCcampus] Digital Pedagogy Toolbox blog series, Gwen Nguyen discusses the potential benefits artificial intelligence (AI) tools like ChatGPT can bring to the field of post-secondary education.
Practical Responses To ChatGPT And Other Generative AI. Montclair State University.
Late in 2022, OpenAI’s ChatGPT prompted a flurry of commentary from doomsday predictions to enthusiasm for creative and educational possibilities. Few instructors have not heard about the tool as stories of it, its rivals, and its cousins (e.g., Google’s Bard) abound in the popular press. It’s unclear to most readers whether or not this generation of AI has really passed the Turing test, whether we’re at a watershed moment, but instructors will benefit from giving thought to the tool’s capacities to generate readable prose and calculate problems, among other tasks.
Rigolino, R.E. (2023). With ChatGPT, we must teach students to be editors. Inside HigherEd.
Teach Online. AI in Higher Education Resource Hub.
Covers Latest Developments; Background on AI; Experiences, Creation, Support; Assessment, Grading, Examinations; Policy and Concerns
Teaching with Generative AI
Assessment Strategies and Considerations
5 Strategies for GenAI Rubric Proofing [Infographic]
Sample Rubric for Mitigating GenAI Use [Pdf]
Sample Performance Indicator Language for Mitigating GenAI Use [Pdf]
Resources and considerations for designing rubrics that mitigate against student use of GenAI for assignment completion.
“Leaning In” Criteria for GenAI Rubric Design [Infographic]
“Embracing” Criteria for GenAI Rubric Design [Infographic]
Sample Performance Indicator Language for “Leaning In” and “Embracing” [Pdf]
Resources and considerations for designing rubrics that allow for student use of GenAI for assignment completion. Following Academic Integrity’s GenAI Syllabus Language (Example 2 and Example 3).
Prompts and Ideas
Embracing Generative AI in Post-Secondary [Google Doc].
Ideas for using generative AI to support students and learning and ideas for using generative AI to support teachers
Herft, A. (2023). A Teacher’s Prompt Guide to Chat GPT: Aligned with “What Works Best” [PDF].
This [is a] short instructional teachers guide to using ChatGPT… By following this guide, you will learn how to effectively incorporate ChatGPT into your teaching practice and make the most of its capabilities. We will provide specific examples and strategies aligned with CESE NSW’s “What Works Best” to help you get started.
The following resources provide a comprehensive overview of the capabilities and limitations of ChatGPT and GenerativeAI. The intention is to support faculty through practical knowledge of the tools available and the extent to which they want to implement or mitigate GenerativeAI use in their course.
Resources from BC Campus
September 19-25, 2023
September 28, 2023
June 17-21, 2024
Please note: Some of the above strategies are outside our recommended guidelines for use.
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Need help?
The Teaching and Learning Commons have resources and strategists who can assist you with design of learning activities and assessments. For assistance please contact TLCommons@kpu.ca