10 Key Technological Developments Accelerating and Changing Teaching, Learning, Assessment, Student Support and Research

10 Key Technological Developments Accelerating and Changing Teaching, Learning, Assessment, Student Support and Research


One key development in higher education in 2023 was the realization that technologies such as AI and XR immersive experiences can have a major impact on teaching and learning. The reaction to the launch of ChatGPT 3 in November 2022 caught many by surprise, even though generative AI is not new and large language model chatbots have been with us since the mid-1960s.


As technology development accelerates and its impacts on teaching, learning, assessment, student support and research become more significant, it is critical for higher education to understand both what is happening and what will soon happen in order to anticipate a different future. The need for anticipatory governance and strategic approaches to IT are becoming increasingly evident.


Contact North | Contact Nord does not make predictions. Instead, we document demonstrable trends and patterns and raise questions to be explored by colleagues across the higher education ecosystem. By identifying trends and patterns emerging from developments in technology, the sector can better prepare for the emerging future.


10 key technological developments


The exponential growth of AI-enabled tools and resources poses both challenges and opportunities for the higher education sector. Between 20 and 30 new AI tools are released each working day (there are now more than 10,000), and existing AI systems such as ChatGPT, Gemini and Claude are being constantly updated and improved. Institutions of higher education face the dual challenge of potentially lagging behind or racing too far ahead of the education sector when it comes to adopting and implementing AI, which can impact their market acceptance.


Given the wide range of technological developments, we focus on 10 key developments which have the potential for significant change in the way colleges, universities and Indigenous institutes operate. The first five directly affect students while the others impact faculty and instructors as well as administrators.



Developments Affecting Students


1. Predictive analytics


Using all available data about students and their behaviour (e.g. from student information systems, learning management systems and client relationship management systems), predictive analytics can increase student retention and completion rates, focus student support resources where they can have the most impact and lower the completion cost per student.


Examples suggest retention rates can be increased by 11%, graduation rates by 34% and cost savings can be substantial.


In some institutions, such as Rio Salado College, analytics focus on the work of student support and advising staff. Targeting individual students, the analytics suggest those who are struggling or not meeting performance expectations. In other cases, such as The Open Polytechnic of New Zealand, students themselves are being alerted to the way their current behaviour may need to change for them to be successful. In this case, AI is used to empower and encourage student agency.


2. Virtual assistants for students, faculty and administrators


Chatbots have been used since 1966 as tutorial assistants for students. The story of Jill Watson (aka IBM Watson), first deployed at Georgia Tech in 2016, drew attention to the potential of 24×7 quality student academic support.


Chatbots are easier to build and deploy and can now be “trained” on specific academic content in a few days rather than weeks or months. They contribute to refining both the learning process and teaching methodologies that can enhance learning outcomes. While some are focused on specific content domains (e.g. Athabasca University’s Freud Bot), others can be used to develop skills such as critical thinking, data analytics or comparative analysis. In 2023 Contact North | Contact Nord released a free-to-use chatbot called AI TutorPro, which supports any student at any level in their learning.


3. Adaptive assessment and learning engines


All the major learning management systems have adaptive assessment engines embedded within them, which adapt and customize the questions asked based on a student’s responses and performance. As a student answers questions correctly, the questions get harder. If a student struggles on certain questions, the test provides easier questions. This approach has several key advantages:


  1. It provides a more accurate picture of a student’s abilities by targeting questions at their level of knowledge, skill and capability.
  2. It permits instructors to identify gaps in student learning and to develop targeted remediation.
  3. It improves engagement and motivation.
  4. The customized difficulty level allows advanced students to be challenged rather than held back by questions that are too easy. This enables gifted and highly capable to students to move through their courses faster while the less capable students receive the support they need to be successful.
  5. Reports from such assessments provide rich analytics to inform teaching, improve course design and to rethink some activities — all vital for continuous improvement.

The deployment and use of adaptive assessment supports differentiated instruction and design justice.


Also important here is the power of adaptive learning systems. A specific example is the use of simulations, especially in XR environments. Students experience “real world” situations and the systems respond to their actions in real-world ways. Whether these are virtual co-operatives, health simulations, pilot training or explorations in an immersive landscape, students can have truly visceral experiences as the smart systems adapt to their actions.


New technologies, such as Apple’ Vision Pro headset powered by AI, can enable faster and more meaningful simulations that adapt in a variety of ways to the actions students take.


4. Simultaneous translation and related services


As more international students find their way into Canadian colleges and universities, campuses are becoming more multicultural and classrooms more complex. One barrier to effective teaching and learning is language. For some students, English or French are their fifth or sixth language.


With all of the generative AI models (ChatGPT, Gemini, Claude2, etc.) are able to translate from text, audio or video files, listening to a lecture or workshop in one language but reading or hearing it in another is now easier than ever. In addition, smart devices such as the AI Smart Recorder enable a student to receive simultaneous translation in up to 140 languages.


There are limitations. For example, none of the engines currently permit translation into indigenous languages, such as Cree. Active research is under way to explore how tools like neural machine translation models could aid the preservation and revitalization of endangered Indigenous languages. This includes efforts by Indigenous technologists and linguists to build custom models and resources tailored to their languages‘ structures.


5. Support for writing and literacy


Student writing and literacy levels are problematic, according to a study by the Higher Education Quality Council of Ontario. Most colleges, universities and Indigenous institutes have writing support services to help students who struggle with writing.


AI tools, like Grammarly or Hemmingway, can provide 24×7 support to improve writing and grammar. Generative AI systems, like ChatGPT or Claude 2, can also be used to improve student writing, comprehension and understanding. More specific tools, such as Essai.Pro or Bot Writing for essay writing or Creative Writing Coach for more imaginative writing are also widely used.


Given how important literacy and writing are, the deployment and use of these tools need to become more ubiquitous and affordable.



For Faculty and Instructors


6. Differentiated instruction for equity and inclusion


The development of AI and related technologies enables colleges, universities and Indigenous institutes to reimagine teaching, learning and assessment based on taking action to directly address issues of equity, diversity and inclusion.


Design justice is an approach to learning and assessment design that seeks to challenge structural inequalities and advance collective liberation. In higher education, design justice involves rethinking pedagogical and assessment practices to be more inclusive, equitable and just.


The “one size fits all” approach to teaching and assessment is a key barrier to equity and inclusion. With AI tools and the growth of blended (hybrid) learning, differentiated instruction and individualized or group-based assessments that reflect students’ preferences for how they wish to be assessed become possible.


Each student may also choose different learning pathways and materials to achieve the outcomes of the class or program of studies. Some may wish to read texts while others prefer multimedia content. Some may want to pursue their studies in a language other than that of instruction. All this becomes possible with AI-enabled tools and technologies.


Faculty leverage constructivist pedagogy. In such pedagogy, teachers act as facilitators to help students achieve learning goals using problem-based and project-based learning.


Problem-based learning works best when problems are part of larger, ideally real-world tasks. Students are supported to take ownership of the problem, the task is appropriate to the level of understanding and ability, the student must reflect on what is being learned and how they learned it, and the educator encourages the student to test their ideas in various contexts. Students develop both cognitive and affective skills and capabilities and engage in authentic work, which best prepares them for the work of active citizens and engaging future employees or entrepreneurs.


Rather than book learning (which may still be part of the learning process), students learn through work-integrated learning, community action and projects that challenge them to apply their learning to real-world problems and challenges. The Technological University of Monterrey redesigned both its business and engineering programs to be based on this approach to teaching and learning.


7. Course and learning materials creation


Before the COVID-19 pandemic, the general assumption was that an individual faculty member was responsible for the design, development and delivery of “their” course. Exceptions occurred when a team taught a course or when the course was designed for online learning by a team that which included instructional designers, librarians, students and technology advisors as well as faculty. A new course may take at least several weeks to be designed, developed and deployed.


AI technologies can now create courses quickly, with the technology providing an instructional design approach as well as automating materials creation, assessment design and resource finding. Most of the current examples of AI-enabled course creation systems (e.g. Learning Studio AI, AI Course Creator and CourseGen) are highly behavioural rather than constructivist, although this is beginning to change with new approaches such Contact North | Contact Nord’s AI Teaching Assistant Pro, launched in 2023.



For Administration


8. Automated back-office processes


Whether we explore financial services, legal services, the work of the Office of the Registrar or other administrative functions, a range of technologies can now improve the efficiency and effectiveness of these functions while lowering costs.


In particular, repetitive tasks, admissions processes, transcript evaluation and maintenance, and student records can all benefit from a combination of blockchain technologies and AI. Using automated processes that are supported by AI enhancements, routine tasks can be more efficient and additional functionality added.


One example is automating financial audit processes by using AI designed to develop models that explain the decisions they make. Among other uses, this allows blockchain auditors to use AI to monitor and flag transactions and understand why decisions were made. Rather than auditing occasionally, such technologies permit the audit of every transaction as they occur. Colleges, universities and Indigenous institutes can better track and audit expenditures when coupled with blockchain technologies.


9. Predictive maintenance and facilities management


The Internet of Things (IoT), coupled with advanced AI systems, can improve the management and maintenance of facilities and enable smart learning in smart classrooms.


More specifically, technologies can be used to support the work of facilities management and development in three key areas:


Preventive maintenance


  • Computerized Maintenance Management Systems (CMMS) can schedule, track and optimize preventive maintenance routines. This data-driven approach reduces equipment failures.
  • IoT sensors can monitor building systems and detect issues proactively before failures occur. This enables condition-based maintenance.
  • New nano-coatings and filtration technologies can improve HVAC system effectiveness and health/safety.


Sustainable development


  • Smart lighting, HVAC and building automation systems greatly reduce energy usage and costs, promoting sustainability.
  • Technologies like solar, wind, geothermal energy and microgrids allow campuses to use renewable energy sources.
  • Online learning capabilities expanded during the COVID-19 pandemic make education more accessible while reducing commutes. This may require retrofitting classrooms with appropriate technologies for hybrid and hyflex learning and ensuring all new facilities are designed for flexible learning.


Smart buildings


  • Interconnected sensors, devices and building automation optimize energy efficiency, occupancy, security and more in real time.
  • Dashboards provide facility management teams with unprecedented visibility into asset performance to inform maintenance and planning.
  • Technologies enhance classroom experiences with interactive media, lecture capture, personalized learning platforms, an ability to connect and engage students who are class with those “attending” virtually, and more.


10. Pathway advising


Choosing what to study, when and in what mode is becoming more complex. A study in  Calgary showed an adult learner has more than 30,000 learning choices in a city of just 1.3 million people. (That study did not include online courses available across Canada from both Canadian colleges, universities and Indigenous institutes and MOOC providers, such as edX, FutureLearn, Udemy and Coursera.) Individuals need help connecting their learning intentions to the available learning opportunities.


AI systems can be effective at finding and suggesting learning options linked to career, professional or personal preferences. Applying rules and using highly engaging conversational processes, career and learning guidance linked to learning style preferences can be provided at a low cost, 24×7.



What these key developments can lead to


Five key elements of digital transformation, described by EdTech imagineers like Sam Altman (CEO of OpenAI)[9] and change advocates such as Crow[10], Seldon and Adiboye,[11] and Winnick[12], have the power to “transform” higher education.


1. An end to “batch” teaching


Instead of working with cohorts of students in grades or classes, individual learning agendas are developed based on an assessment of the skills, competencies and capabilities the student already has as well as ongoing conversations about the purpose of the learning.


Where does the student want the learning to take them? AI systems, coupled with coaching, mentoring and some instruction and peer support networks. then ensure the student is supported on the carefully mapped learning journey.


Teaching can still take place: students challenged with a set of related issues can be brought together for a boot camp or focused period of instruction before returning to their individualized study activities.


2. An end to “timeas a learning metric


Currently, undergraduate courses are described in terms of credit hours. Learning and assessment on demand, fast-tracking or slow tracking using adaptive assessment, and modular learning when the student is ready liberates them from the time-based metric.


3. An end to exams as we know them


Exams are seen as an efficient way to judge the output of batch teaching but are known not to be good indicators of student knowledge, skills and capabilities.


Not all learners are good at exams. Some perform well by practising test-passing rather than learning the material, but few exams assess competencies and capabilities.


With AI-enabled assessment generation, automated marking and assessment on demand, exams are no longer needed. More authentic assessments that are project-based, team challenges, work-based learning activities and student self-directed projects can become the norm.


4. Increased feedback and advising


AI systems do not have “office hours.” They are available 24×7 and never tire of finding new ways to respond to student queries and needs.


Subject-focused chatbots, which are “fed” all necessary information about a subject, do an excellent job of subject-matter advising and teaching. 


Analytics suggest some students need to be supported by teachers or others with special skills, such as writing coaches, statistical support workers and numeracy advisors. The goal is to ensure every student follows and is successful in their learning journey.


5. Greater accessibility


Higher education systems are organized around blocks of time: semesters or terms.


There is no longer good reason for this. Students should be able to begin their learning at any time and be assessed when they are ready. A four-year undergraduate degree could easily be completed in 16 months by a diligent student, for example, or in even less time by someone arriving in the program with substantial life and work experience.


AI systems can also leverage technologies to ensure the resources and supports for students reflect who they are (culture, language, experience and skill) and what challenges they face (disabilities, learning styles, etc.).



A volatile, uncertain, ambiguous and complex future


The idea that the future is growingly volatile, uncertain, ambiguous and complex has taken hold.


The future clearly will not be a straight line from the past. Leaders and policymakers in higher education need to keep a close eye on emerging technologies, which will change both what we teach, how we teach and how we assess student learning.


There are new opportunities to think and work differently to increase access, lower costs and improve learning outcomes. It will take careful navigation of the future to ensure that technology has these impacts.


See Richardson, V. (2003) Constructivist Pedagogy. Teachers College Record, Volume 105(9), pages 1623-1640.

Crow, M. (2020) The Fifth Wave: The Evolution of American Higher Education. Baltimore, Maryland: John Hopkins University Press.

Seldon, A. with Abidoye, O. (2018) The Fourth Educational Revolution: Will Artificial Intelligence Liberate or Infantilise Humanity. Buckingham, UK: University of Buckingham Press.

Winnick, A. (2023) The Generative Age: Artificial Intelligence and the Future of Education. New York: ConnectEDD Publishing.

Title: 10 Key Technological Developments Accelerating and Changing Teaching, Learning, Assessment, Student Support and Research
URL: https://teachonline.ca/tools-trends/10-key-technological-developments-accelerating-and-changing-teaching-learning-assessment-student
Source: Stephen’s Web ~ OLDaily
Source URL: http://www.downes.ca/
Date: February 21, 2024 at 07:37PM
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