Education Technology and the Campus of Tomorrow


The campus of tomorrow emerges from the rapid adoption of technology for the classroom. Four key technologies will shape that future. They include augmented reality, virtual reality, mixed reality (AR/VR/MR), artificially intelligent education (AIEd), intelligent tutoring systems (ITS), and automated assessment tools. This brief article includes definitions, examples, and vignettes designed to provide the reader with a glimpse into the campus of tomorrow with implications for the workplace of tomorrow as well. We introduce you to our fictional virtual student, Candice Panadera (she/her) and the artificially intelligent, technology enabled campus of tomorrow.

Higher Education is racing toward the integration of a host of high technology applications representing “a digital disruption from within” (Thomas & Thorpe, 2018, p. 63). The pandemic accelerated the trend toward online education and the greater utilization of education technology (EdTech) in the classroom (Valverde-Berrocoso, 2020). While the technology races ahead, educators may be forced to play catch up. In a recent presentation at the National ACBSP Conference, McCleskey and Melton (2022) discussed the impact of recent technological developments on the delivery of higher education. This brief article picks up that thread, discussing four emerging technologies in higher education, their applications, and their effect on the campus and classroom of tomorrow. These technologies include augmented reality, virtual reality, mixed reality (AR/VR/MR), artificially intelligent education (AIEd), intelligent tutoring systems (ITS), and automated assessment tools. The authors present these technology applications here through vignettes that follow the activities of our fictional student of the campus of tomorrow. 

The Campus of Tomorrow

We observe Candice Panadera (she/her) starting her class at Future Business University (FBU). Candice dons her Virtual Reality (VR) viewing device and interactive gloves (both provided by FBU), starts the application software, and joins her virtual classroom. Candice sees herself seated in an Immersive Virtual Environment (IVE) and can move around and interact with objects in the environment, including learning resources, a virtual library, assignments, and various avatars. She uses her gloves to select objects, swipe through menus, and point toward options that appear on her VR viewing device. Actually, Candice is seated comfortably on her couch at home and attending her course asynchronously. 

Augmented Reality, Virtual Reality, and Mixed Reality AR/VR/MR

Augmented reality (AR) combines digital information with the natural world presented in real time (Raushnabel et al., 2022).  Virtual reality (VR) refers to an “immersive, artificially-constructed reality” (p. 2). Researchers often present the two concepts as interchangeable; however, they represent distinct technologies. Any user experience that combines the elements of both AR and VR by presenting both real and virtual objects is called mixed reality (MR). AR/VR and other 3-dimensional technologies, including holograms, represent the next generation of digital learning, also known as Classroom 3.0 (Fourtane, 2021). Currently, AR/VR technology assists in the study of medicine, the physical sciences, engineering, and other educational specialties where the requisite hands-on skills must be developed (Rajeswaran et al., 2018). Prospects for the future growth of AR/VR and MR technologies include using MR for simulated teaching and learning (Tang et al., 2020) and the continued development of expansive IVEs (Fitton et al., 2020). 

The use of VR headsets in virtual environments (IVEs) plays a crucial role in interactive learning activities and student engagement (Ritter & Chambers, 2022). Additionally, AR/VR utilizes technology to create virtual field trips to provide students with exposure to physical locations and location-based learning without requiring a physical visit to those locations. The recent COVID-19-related lockdowns elevated the need for these types of technological capabilities. 

Artificially Intelligent Education (AIEd) and Machine Learning

AI involves leveraging various technologies to create a specific device or construct to accomplish a task that previously required human input (Artificial intelligence: 7 questions…, 2022). Writing for a special report on AIEd titled “Artificial Intelligence: Where are we now?”, O’Brien (2022) stated that AI is already having a significant impact on higher education through adaptive learning to better serve students with disabilities, by offering complimentary access to course materials, and through freeing faculty from transactional tasks to pursue higher levels of impactful student interaction. 

AIEd is a constellation of artificial intelligence and machine learning applications in higher education. Among these are intelligent tutoring systems, profiling and adaptive systems, and assessment and evaluation applications. Higher education institutions utilize AIEd to provide feedback on assignments, provide tutoring, conduct assessments, grade assignments, create personalized learning opportunities, proctor assessments, and detect plagiarism (Brooks, 2022). AIEd can potentially reduce faculty workloads by 20-40 percent and reduce course preparation time by as much as 50 percent (O’Brien, 2022).

The Campus of Tomorrow

Candice decides to attend a tutoring session, so she selects the virtual menu option, her avatar appears, and she calls for her tutor. The tutor avatar appears seated next to Candice’s avatar. “Hola Candice, how can I help you today?” Candice customized her tutor’s avatar as a middle-aged Latina with kind eyes and a wide smile. She speaks with a slight accent and wears a simple cotton dress.  Candice named her tutor Luisa, after her Abuela (grandmother in Spanish).  Her memories as a young girl spending summers in Puerto Rico inspired the customization decisions Candice applied.  The interaction continues:

  • Candice: “Hola, Luisa, I am ready for another session.” 
  • Luisa: “We left off at the beginning of Unit   Six, Organizational Culture. Shall   we start there?” 
  • Candice: “Yes, please.” 
  • Luisa: “Have you completed the reading?” 
  • Candice: “Yes, most of it.” 
  • Luisa: “All right, let’s review.” What is the
      competing values framework?” 

Candice spends the next 30 minutes discussing Unit Six. When she demonstrates that she does not fully understand some topics, Luisa provides mini lectures, suggests video links, and recommends rereading some sections.

Intelligent Tutoring Systems (ITS)

Intelligent Tutoring Systems (ITSs) simulate one-to-one personal tutoring and make decisions about the learning path of an individual student (Luckin et al., 2016). In a meta-analytic review of fifty studies of ITSs, Kulik and Fletcher (2016) found positive results for learning outcomes, with 92% of participants receiving higher post-test scores than their counterparts who did not receive ITS tutoring. ITS opens the possibility of highly customized individual tutoring to underserved students and underrepresented minorities who might otherwise never have access to that level of specialized help (Artificial intelligence: 7 questions…, 2022).

ITS is already in use in some institutions. Previous research demonstrated that ITSs combined with embodied agents (avatars) could help students learn math more effectively and reduce math anxiety for high anxiety learners (Kim et al., 2017). Some ITS systems can provide highly sophisticated writing instruction with an automated feedback system (Nunes et al., 2022). Automated text evaluation is discussed in greater detail later in this paper. 

The Campus of Tomorrow

Candice reviews the research paper requirements in her Organizational Behavior course and composes the first draft of her assignment while sitting on her couch and using a combination of voice dictation, typing on a virtual keyboard using her VR gloves, and a heads-up display editor. Once complete, she drags and drops the draft over to a virtual in-box labeled “writing help”. An automated writing assessment program reviews her writing instantly, and the hologram of the paper appears in the outbox. A floating message icon includes the number 27. Candice is a little disappointed that the writing assessment tool has made 27 suggestions. “OK,” she thinks, “let’s deal with this,” and clicks on the paper to open it and begin reviewing the suggestions related to mechanics, word choice, style, and formatting. One edit at a time, her writing will improve.

Automated Assessment Tools

Automated assessment of student writing, known as Automated Writing Evaluation (AWE), involves using technology to evaluate and score written text (Nunes et al., 2022). The latest advances involving machine language allow for sophisticated feedback and scoring based on latent semantic analysis using natural language processing (NLP). NLP methods include the capability to provide feedback on mechanics, grammar, word usage, style, structure, variety, and coherence of writing. Higher education institutions utilize these tools as both evaluative and formative assessment tools, and the applications include a feedback engine designed to offer qualitative guidance on how students can improve their writing.  

Technology as a Critical Bridge

Technology has been called the “critical bridge that connects the student and the instructor” (McCleskey & Melton, 2021, p. 315). This is particularly salient in the post-COVID-19 reality of virtual learning environments, asynchronous resources, and technology-enabled instructor engagement and immediacy. However, some scholars still refer to the promise of AIEd as hype, at least for now (Brooks, 2022). One reason potentially involves a lack of sufficient readiness for the latest technologies on the part of universities (McCleskey & Melton, 2022). In contrast, other scholars argued that higher education is uniquely capable of utilizing technology engagement and developing ways to cultivate AI and machine learning to solve the world’s most pressing challenges (Fleming, 2022). With the number of U.S. virtual workers expected to grow from 5 million pre-COVID to more than 25 million in the near future (Ramlall & Cross, 2022), the time is right to embrace the technologies that will guide both the campus and the workplace of tomorrow.