My first introduction to the virtual interior began in 1989 as a graduate student at Ohio State University. As an undergraduate in 1983, I completed a weekend intensive computer-aided design and drafting – emphasis on drafting – course, but it was not until I modeled a three-dimensional digital interior that I fully understood the implications of the computer for design. At OSU, I grimaced my way through the entire semester, manually inputting XYZ coordinates into this black box while contemplating how I could complete the task much faster using foam core and balsa wood to build the model. My “ah-ha” moment arrived during finals week – in less than 20 minutes I was able to output five detailed perspectives from very diverse points in space. It was this revelation that guided the next 20 years of my academic career exploring the virtual interior.
Defining the virtual interior varies greatly among authors, academics, practitioners, and students. I take the broadest point of view and adhere to the idea that any design task which engages a computer is in effect initiating a virtual interior component. Therefore, everything from employing Photoshop to render a sketch to constructing a study model with SketchUp or composing a flyaround using 3D Studio Design are all justifiable activities in shaping and giving identity to the virtual interior.
As someone trained as an interior designer and not as a computer programmer, my interest was not in writing new code or developing new software. Instead my interest was to be inventive and explorative with existing software applications commonly used in the fields of architecture and interior design. I taught numerous requisite AutoCAD and digital media courses in multiple universities and found my greatest pleasure in authoring new ways to teach and conceptualize the virtual interior. I never used textbooks in my classes because they were too rote and prescriptive, giving the impression that computer-aided design was only technical and could not be used as an artistic medium. I credit the writings of Mumford with inculcating this belief in me when he made the distinction that a “machine lends itself to automation and routine actions, whereas the tool lends itself to manipulation and use as an extension of the user” (Mumford 1934; Muffoletto 1993).
In contrast to many interior design departments in the early 1990s, I taught digital skills alongside manual skills in one required introductory graphics course – I wanted my students to experience and envision digital software applications on par with traditional tools and without any preconceived identity when choosing an appropriate tool for the task at hand (Gibson 1994; Gibson and Sipes 1993, 1995).
Experimentation in the classroom revealed a unique design pedagogy when marrying the computer with creative activity. Empirical evidence showed an increase in speed and precision of basic hand drafting if entry-level students learned AutoCAD prior to manual drafting instruction. Testing my hypothesis over a three-year period (1995–1997) revealed that knowledge of CAD increased manual drafting speed by 25% and produced architectural hand drawings with superior drafting conventions. These results came through the evaluation of student work. When I taught manual drafting first using traditional tools and methods, scores followed a standard bell curve. When AutoCAD was taught first, the scores for manual drafting exercises were significantly skewed higher. The first year, I hypothesized the better scores were an anomaly, but the same outcome happened in subsequent years. The explanation centered on the fact that students expected their manual drawings to match the precision they experienced with AutoCAD. Assessment of speed was based on when students submitted their work for evaluation.
Beyond speed and precision, students demonstrated a difference in project knowledge and level of detail when using AutoCAD versus traditional drafting tools. For example, after using AutoCAD, students could quickly recite dimensions of walls and column spacing from memory, which was not the case when I queried students after completing a manual drafting exercise. It seemed typing actual dimensions into the computer versus using an architectural scale achieved a level of resonance for the students. These differences were unanticipated by me, and yet the student evidence was so overwhelming that it encouraged me to experiment more with digital media in my course instruction (Gibson 1996, 1999).
Benefits were also revealed with three-dimensional design tasks. Modeling software, e.g., 3D Max Designs and SketchUp, encouraged students to apply design decisions volumetrically and strengthened mental visualization abilities. For example, students would routinely tell me that the software forced them to immediately consider implications for walls and ceilings as they manipulated the design of their floor plan. Software applications also provided features rarely available with manual tools, such as shadow-casting, interior illumination, and walk-through animations. My pioneer work, along with that of my graduate students, included morphing as a new method for schematic design, morphing as a means for teambuilding, lighting simulation and user preference, and evaluating wayfinding cues during emergency situations (Chayutsahakij 1998; Gibson 2000, 2001, 2003a, 2003b, 2007; Gibson and Sipes 1995; Jung and Gibson 2006). These projects will be discussed in greater detail in the following section.
For several years I experimented with gaming software applications to explore whether they could provide meaningful advances for the virtual interior. During this period in my career I expended a lot of energy learning new software and developing engaging project scenarios for class instruction with very little tangible return. I witnessed the volatility of software development – as quickly as programs arrived, they disappeared. I was frustrated, having expended significant time and energy with minimal benefit. Thus, I refocused exclusively on proven industry-standard software applications for future pursuits.
In 2006 I taught an advanced digital media course, and for the first time used AutoDesk's Revit software, a building information modeling (BIM) package. Revit was unlike any CAD application I had ever experienced. Its supremacy over other three-dimensional programs revealed itself in the depth of data linked to each modeled object. Walls were no longer simple rectangles or vacant boxes but were populated with information about material, mass, and weight. Libraries of doors, windows, and furniture inherently contained accurate manufacturers' product information. At the end of the project, my students and I witnessed how effortlessly Revit accurately tabulated a window schedule and reported the total square footage of carpet to purchase. I marveled at the reality of BIM – a remarkable software that seized the long-held promise of computer-aided design as a truly revolutionary design tool – had finally arrived on my desktop computer. In that moment I envisioned a different future on the horizon for my students. BIM was a sea change – it was time to revise not just my digital media courses, but the way in which we imagine interior design education.
As a personal challenge from a colleague who insisted that computers were only useful in the final documentation stage of the design process, I began to explore if computers could, in fact, aid the creative design process. A friend gave me a copy of Digital Dreams, which I read many times over (Spiller 1998). The concepts were foreign to me and I struggled to digest what it might mean for my teaching and the field of interior design. I turned to books by Nicolas Negroponte and Silicon Mirage by Aukstakalnis and Blatner. Subsequently, the exhibit Folds, Blobs + Boxes: Architecture in the Digital Era advanced my resolve about the virtual interior (Rosa 2001).
In 1998, my discovery of Would-Be Worlds by John Casti became the catalyst for a series of experiments with digital morphing (Casti 1997). I investigated two-dimensional software programs popular for morphing two incongruent images together, but opted for a three-dimensional application to produce more tangible volumetric results. Through trial and error, I taught myself how to use the morphing features of 3D Studio, and I began to postulate how the automated traits of the computer could be creatively employed during the schematic design phase.
In his article “Creativity as a Mechanical Process,” Schank compared human thinking to artificial intelligence (AI) algorithms, postulating that both subscribe to a set of rules for creativity to take place. He suggested that computer usage may amplify experience patterns (XPs) which would in turn encourage more abundant cognitive associations through the exploration of what-if scenarios (Schank 1988). I began to hypothesize, and asked myself: What if computers could perform mathematical operations on form, such as combining a percentage of several building facades into a single visual composite? Or what if the computer could combine two interior spaces – one that is more efficient with one that is more aesthetic – into one hybrid solution? If computer use can amplify experience patterns and encourage more abundant cognitive associations through the exploration of these what-if scenarios, I surmised they may have a positive impact on creative thinking for interior design.
Utilizing what I had learned from reading Casti's book on artificial worlds, I tailored my knowledge of morphing processes to the design disciplines, which I named “Cyber-Ideation” (Gibson 2000, 2007). In 1998 I introduced the concept into my junior interior design studio as a method to stimulate schematic ideation. Working in teams, students were asked to model an interior object, such as a chair, table, or lamp, and also a portion of a favorite modern building. These two disparate models were akin to two parents. The parents were then morphed together to produce hundreds of hybrid images (aka children).
Students assessed the hybrid images and selected one that would serve as the origin for their schematic ideation. Students morphed the origin two more times (grandchildren, great-grandchildren), once using traditional manual sketching techniques to incrementally transform the origin into iterative impressions and the other time using the computer to once again morph two dissimilar models together. We asked the student teams to create a family genealogy chart to document four generations of image occurrences that developed from the two-parent “seed” models.
At the conclusion of each morphing sequence, students would evaluate the numerous hybrid images and select one to move forward in the ideation process. The sheer quantity of images produced using “cyber-ideation” was unusual for the students and at times overwhelming for evaluation. After the experiment, students noted that they were generating more unexpected and unique ideas to explore when compared to their traditional method of ideation sketching. They acknowledged that use of “cyber-ideation” produced innovative form and surface color and texture that would not have been realized through traditional conceptual ideation techniques. Team dynamics improved in that all members contributed equally, and the computer process was viewed as an objective and unbiased regulator.
After completion, assessing the “cyber-ideation” process and debriefing with student participants revealed both expected and unexpected results. First, “cyber-ideation” was successful at breaking habitual thought patterns. Students who routinely struggled with idea generation swiftly realized the potential of this mechanized process. Feelings of impotence and lack of creativity were replaced with authority and control. The process formally encouraged a level of serendipity resulting in unplanned outcomes.
Students wanting a more conventional ideation procedure found the open-endedness of “cyber-ideation” disconcerting. The vastness of hybrid output was initially overwhelming for students to evaluate. Quantity, however, quickly became a benefit – the realization that an infinite number of alternatives could be generated made students less married to their first and second choices. Within this procedure, students also became adept at appraising small details – selections based on minute variations that appeared in neighboring iterations.
Personal style was minimized with this ideation method. One explanation may be attributed to the repeated merging of images, blending together several voices as in a choir. Students professed that this exercise produced work that was unexpected and more diverse than if they had been left to their own traditional work patterns. Substantial differences in computerized morphing and manual sketching ideation techniques were observed. Whereas the computer required a closed process – two seeds – to successfully accomplish the morphing process, hand sketches were open-ended.
Comparing their experiences, students believed the manual iterative process to be more difficult and time-consuming. In fact, most students underestimated the time it would take to generate 14 iterations by hand even though they were more experienced with manual sketching methods from previous studio classes. Another noted difference was that students did not choose to specify materials or color in their hand sketches, whereas these were major elements in the digital models. One explanation may be the seductiveness of the software's digital library to raise students' curiosity with surface characteristics. Another rationale may be that conventional thumbnail sketches are typically intended to study form and usually do not possess information about materials until further into the design process.
One unanticipated outcome was in the area of team dynamics. Ineffectual students who routinely had their ideas discounted in traditional teaming processes felt “cyber-ideation” neutralized the playing field. The mechanized process gave every team member an equal opportunity to contribute hybrid forms to the discussion. Another unforeseen result was that several students experimented with “cyber-ideation” in subsequent studios for idea generation. Individual examples included the mixing together of disparate cultural artifacts to find hybrids that contained a percentage of African and North American roots. Another example combined 18th- and 20th-century furniture into a composite piece. Value was realized by students or they would not have chosen to utilize this technique later in their own individual work.
For design education and practice, “cyber-ideation” is another tool to stimulate creative thinking and discovery. It utilizes the computer's strengths – of rapid calculation – in an innovative way. It also challenges one's perception of using the computer solely for documentation purposes at the end of the design process. “Cyber-ideation” may, in fact, increase experience patterns with the computer, which may augment the way in which computers are conceived of and utilized for interior design. It may lead interior designers out of their habitual mode of practice and toward more novel and suitable design solutions.
My next experimentation with the virtual interior came in 1999 with my assignment to teach a retail studio. As a frequent online shopper, I wanted to compare and contrast traditional bricks-and-mortar stores with e-retailing. According to a Pew Internet report (Horrigan 2008), more than 93% of American Internet users participate in e-commerce activity. This purchasing behavior translates into 875 million global consumers with a penchant for convenience and breadth of choice (Nielson Online 2008). Marketing researchers Davis, Wang, and Lindridge found differences between Chinese and American online shopping behaviors and the physical cues, or atmospherics, which impact consumer decision-making (Davis et al. 2008). What I discovered, however, was that no architect, interior or Web designer had made a connection between virtual and physical shopping environments with atmospherics and behavior.
An extensive design literature identified a relationship between people and their physical environment, and yet a similar empirical focus for the human-online design experience was indiscernible. For example, research found that people prefer physical landmarks to signage as a method to navigate the built environment (Passini 1984). I wondered if this same knowledge could be applied to the navigational system for online websites, or were the two “environments” different? What is known is that e-retailers can lose potential sales because of convoluted websites (Hausman and Siekpe 2009).
The method I chose for this case study paralleled that utilized by Lynch in The Image of the City. Like Lynch, my goal in this project was to investigate the idea of structure and imageability in the environment (Lynch 1960). While Lynch centered on the physical city, this exercise concentrated on the World Wide Web with a purpose to better understand consumer behavior and usage of e-retailing sites. Both studies focused on place-oriented environments on a grand scale – the urban landscape and the virtual landscape. Both used field reconnaissance as an instrument for data collection. From website analysis, students documented paths, districts, nodes, edges, and landmarks in 16 e-retailing sites. Wayfinding errors, facilitators, and obstructers were duly noted (Gibson 2003a, 2003b).
The site www.puma.com, for example, had six districts under the Sport heading. Some of Puma's districts were bilingual, others were geographically defined. Merchandise offered for sale was specific to each of Puma's districts: for example, different merchandise was sold on non-English districts within the Puma website. “Paths” were the most common element and were identified as either lineal, radial, networked, meandering, or a composite (Ching 2007). Students classified online examples as “dead end” when the path terminated at a destination and one had to retrace through use of the back button, or “cul-de-sac” when the path motion did not end but looped so that travel continued, but in the opposite direction.
The most identifiable online “edge” is the requirement of a plug-in, such as Micromedia Flash. Without the required plug-in, the site is like a gated community, impenetrable without permission. Log-ins, memberships, and registration requirements function as physical boundaries, monitoring entry and exit. Dead-end conditions and language also serve as edges, interrupting continuity of motion.
Strategic points of entry characterize the “node.” In e-retailing sites, the shopping cart is a node, a junction of special prominence. At online auction sites PayPal® and Bidpoint become nodes in the e-retail landscape.
The most prominent “landmark” in e-retailing environments is the company's logo. It is generally visible from many locations, no matter where one navigates throughout the site. Landmarks that occur at intersections or decision points along a path enhance their prominence in the environment. A sequential series of landmarks aids consumers, first by triggering action cues and then by reassuring and confirming that past decisions were accurate. On the www.donnakaran.com website, a student noted that changes in color occurred when actions were taken as a method to encourage consumers that site tools were used correctly.
In addition to the five primary categories noted above, Lynch also identified three additional incidences: element interrelations, image quality, and shifting images (Lynch 1960). Images that differ in scale, viewpoint, time of day, season, and/or weather conditions are classified as shifting images. While external conditions such as changes in weather and time of day are absent in e-retailing sites, alterations still occurred. Most notable was how frequently websites are modified and transformed, how they evolve and then expire. Whether offline or online, retailing is constantly in flux given new products and seasonal merchandise. Constant change leaves little opportunity for the retention of path traces and other cognitive mapping cues.
The blurring between online and offline, physical and virtual atmospherics is yet another interesting outcome from this project. Even when consumers prefer making a physical store purchase, 70% had researched products online prior to buying, which strengthens the argument that the Web experience is part of a broader cross-channel branding and selling strategy (Underhill 2010). As retailers see online and offline sites as similar branding entities with common goals and complementary design requirements, both retailing venues merge into a single unified concept and solution. According to Lynch, “[t]he final objective of such a plan is not the physical shape itself, but the quality of an image in the mind” (Lynch 1960).
To fulfill the studio component of the course, students utilized their navigational and wayfinding knowledge of a particular e-retailing website in the design of its physical retail establishment. Relying on the website analysis from the first project phase, students explored schematic ideas for an offline, physical archetype of the online store. Appraisal of Web real estate for the e-retailers was collected by students and informed the allotment of physical area for merchandise and services in the bricks-and-mortar store design.
Issues of hierarchy on the e-retailer website also informed students' decision-making. Websites with an active introductory Flash page challenged students to create a similar experiential entry in the physical store. All students labored to bring characteristics of Web interactivity into their offline store designs. Solutions included ways for consumers to access additional information and receive similar product suggestions by touching a flat panel screen. One student utilized a hand-held scanner to replicate the placing of items into a virtual cart. At checkout, all items scanned would be pre-packaged and waiting for the shopper to take away. Another student retained the hologram changing-room feature from her e-retailer and highlighted this element in her bricks-and-mortar store design.
In all student work, landmarks, nodes, and districts were well demarcated to support a customer's retailing experience. Final design solutions were digitally constructed using AutoCAD and 3D Studio VIZ software, and an animation sequence was rendered. Students presented their projects to a jury of student peers and faculty, who noted the strength of wayfinding attributes throughout the student work and the branding parallels between the online and offline shopping experiences (Gibson 2003a, 2003b).
This case study suggests that the field of interior design is broader than just the physical space – that virtual environments are also within the realm of interior design expertise. Knowledge of human behavior coupled with design expertise in four dimensions gives interior designers an exceptional perspective in evaluating atmospherics in both offline and online environments which are experiential, manageable, and imageable.
A broad literature exists in defining the relationship between store environment and consumer behavior. Bitner suggested that store atmosphere alone can amplify a business's success or failure (Bitner 1992). Among numerous factors that influence consumer patronage, Baker noted that design cues had a greater impact on store choice than other ambient characteristics, and Keller found the interior environment had a more immediate effect on decision-making than other forms of advertising (Baker 2002; Keller 1987).
Between 1996 and 1998, I supervised graduate student Praima Chayutsahakij with her research project, which investigated the impact of artificial lighting design on visual preference in a retail interior (Chayutsahakij 1998). Based on theories from social and environmental psychology as well as research on lighting design, a virtual model was constructed in 3D Studio for the experimental setting. Simulation was chosen over an actual setting to gain control over multiple variables, cost, and time. Lighting options were identified through a visual analysis of interior design trade publications and were codified into six different categories of illumination: direct illumination from the ceiling, indirect illumination from the ceiling, direct spotlight, indirect spotlight, direct radiant forest lighting, and indirect radiant forest lighting. Individual lighting schemes were simulated and calculated using Lightscape software.
Based on the six lighting categories, combinations resulted in the production of 60 unique images, which met the reliability criteria for Q-sorting. Images were created on film via the use of a film recorder and then printed via a color lab, paying close attention to color accuracy. Throughout this process, experts were consulted to evaluate all images and ensure they represented typical retail interiors and intended lighting effects. Subjects (n = 200) ranked the images using a one-way structured Q-sort along four dimensions (preference, mystery, complexity, image) and three levels (low, medium, high).
Subjects preferred interiors with direct illumination, while environments with indirect and radiant forest lighting were less preferred. Preference was related to complexity and mystery; however, mystery was found to be a negative predictor of preference for lighting design in this retail interior (Chayutsahakij 1998).
As evidenced in this experiment, virtual interiors have broader implications than just realistic representations of design ideas. Computer simulation can be a valuable tool to propose questions and research interior design hypotheses. In this case, it was a successful tool in assessing shoppers' preferences for retail lighting solutions.
Beginning in 2000, I supervised graduate student Jin Woo Jung on his research project, which focused on improving wayfinding and exiting during emergency situations (Jung 2002). Chertkoff and Kushigian noted that in the Beverly Hills Supper Club fire, human behavior was a contributing factor in loss of life. Because it is too difficult and dangerous to examine human behavior in actual emergencies, computer simulations have become an effective tool endorsed by many researchers (Chertkoff and Kushigian 1999).
Preparing the virtual reality (VR) simulation tools for this experiment involved building a three-dimensional model in AutoCAD, exporting the wireframe into 3D Studio Max to add surface materials and texture, exporting the 3D Studio Max file into World Up 4 software to define illumination and smoke properties within the virtual environment (VE). Avatars were developed in World Up 4 to enable participants to move through virtual space, and scripts were written to record subjects' behavior at choice points and to measure time. Fifty-two sub-areas were defined in the VE test site to evaluate where subjects would travel. Simulations for this experiment were similar to computer games in which users move themselves through space using a keyboard or mouse.
Samples of landmarks were gathered from various interior design magazines. They were sorted and classified, and finally they were analyzed to ascertain notable characteristics and identify alternative characteristics not previously reported in the literature. In analyzing the variety of landmarks, it was discovered that describability was a potentially important characteristic that prior work had not thoroughly examined. For example, nearly all potential landmarks have distinctiveness, such as unique shape, pattern, color, or history; however, if landmarks cannot be easily explained by words, people will not be willing to verbally use them in route communication.
Independent variables for this experiment were gender, smoke, and landmarks. Subjects (n = 69) were given five minutes to read and memorize a route description to a designated exit. After memorizing the route, subjects were asked to find the exit in the VE by using their memory. Arrow keys were used to move ahead and change direction; mouse clicks opened doors and enabled subjects to exit the building, which ended the experiment. After 15 minutes, if subjects had not yet found the exit, they were allowed to re-read the route description. The entire process was videotaped. Five standardized tests were then administered to ascertain mental cognition, attention fatigue, and mood. Demographic data were also collected with a questionnaire that identified gender and inquired (self-report) about wayfinding experience.
Results suggest that the describability of landmarks was a positive influence to route communication and increased participants' route recall. Individual factors, such as gender and travel experience, were also found to have a positive influence on participants' wayfinding abilities. Findings from this study, however, indicate that smoke condition did not significantly affect participants' wayfinding performance. Researchers hypothesized that while simulation was useful, in its present form it was impossible to replicate the same level of realism (sound, smell) found during an actual fire emergency (Jung and Gibson 2006).
While simulation is known as an ideal method for observing and measuring human interactions, this research project was unable to accurately replicate the totality of an emergency experience. As VR applications become more cost-efficient and less time-consuming to master, they will provide greater opportunities for designers and planners of behavioral research to study interior environments.
As of 2007, I found a different group of students enrolled in my studios – there was a sea change on my doorstep. Born after 1982, millennials are characterized by a lifestyle immersed in technology. At 27%, they compose the largest generational population in the United States, exceeding the baby boomers by 4% (Tapscott 2009). The numbers are more significant in other countries. This group of young adults is more digitally connected than previous generations. Seventy-six percent of students use instant messaging (IM) and spend on average 35 hours per week messaging (Junco and Mastrodicasa 2007). Sixty-nine percent have a FaceBook account; 62% rely on mobile phones for video, music, and entertainment (Schiller 2007: 267). Both genders spend nearly eight hours gaming per week (Winslow 2007).
In 2009 I conducted a small pilot study to investigate the ways in which the millennials are impacting interior design education and practice (Gibson 2012). Using the Boyer Report and Tapscott's Grown Up Digital to guide my methodology, an open-ended survey was created and administered to nine design educators and practitioners of varying age, gender, culture, and geographic location (Asia, North America) (Boyer and Mitgang 1996; Tapscott 2009).
If Kevin Carey is correct that the printed page is the canary in the coalmine and all traditional methods of information delivery – including current instructional methods used in design education – are at risk of becoming inconvenient or irrelevant, then educators and administrators must begin thinking aggressively about the future (Carey 2009). Reflection caused me to contemplate Tapscott's eight norms. Some may contest this assessment of the academy, but I propose that universities currently excel in the following millennial norms: “scrutiny,” “integrity,” “collaboration,” “innovation” and “speed.” Areas in which they are significantly lacking are “freedom,” “customization/personalization,” and “entertainment/fun.” These three norms may provide a path for design education in the future.
Freedom equals choice. Further development of distance learning technologies may enable greater flexibility for the design student. Course selections may no longer be geographically limited to a single physical campus. MIT is advancing this concept; however, operationalizing this worldwide will be more complex to navigate than the elective offerings of one university. In 20 years, I anticipate students will be admitted into a global university and select classes and teaching methods pertaining to personal preference as well as tailoring instruction to individual learning style. Maximum freedom will engage the millennials and post-millennials in customizing their path for learning, which may or may not be a serial endeavor.
Personalization is about taking ownership and controlling one's environment. Today millennials use the Internet to customize their news, shopping venues, and social activity. In two decades, design students will create their own class assignments and learning objectives. Professors will operate more as advisors or personal coaches, offering guidance, encouragement, and evaluation when needed. Students will take more responsibility for learning through personalized instruction.
Entertainment is about stimulation and pleasure. For the post-millennials, digital technology will be more interactive and employed at the beginning of the design process instead of at the end for documentation. Curriculum sequences used today will be flipped in the future. BIM is the start of this trajectory – of flattening and reorienting the design process. Reduction in duplication and cumbersome translation between two- and three-dimensional records will result in additional time for exploration and design development (i.e., fun). Time seized in the past decade to learn computer applications will evolve into mastering one comprehensive and universal software package during the first year. Moreover, design educators in 20 years' time will be millennials. Methods of working and living will ultimately change to conform to their understanding of the world.
The three future scenarios presented above seem to parallel the “design synthesizer” and “design accelerator” provocations as outlined by The Future Designer ThinkTank (Victoria and Albert Museum 2008). Design synthesizers assemble what they need into a broad-based collaborative structure for problem-solving. Inherent in the synthesizer's repertoire is the sense of customization and freedom – the ability to shape a design team according to specific talents, viewpoints, experiences. Similarly, the millennials will shape their education team by taking courses at different universities, with different faculty members, and within a desired time frame. Design accelerators are known to challenge existing paradigms and traditional methods of practice. Like millennials, accelerators would advocate play and experimentation in their work processes, in search of discovering/reinterpreting new design models, services, and design processes for creating the virtual interior (Gibson 2012).
My 20-year academic journey with computers and virtual interiors encompasses a full range of instructional exploration and innovation along with empirical and epistemological research in search of progressing the field of interior design. Some inquiries began with a question based on the writings of diverse authors, other investigations reacted to intolerant viewpoints about computers and creativity. “Cyber-ideation” responded to widely held beliefs that computers were isolating and prescriptive. My findings demonstrated that variation exists in computer use, and methods can be authored to benefit teaming dynamics and creative exploration. The online-offline retail project used the computer to draw comparisons between mental cognition and virtual and physical wayfinding.
Empirical findings suggest that a connection exists that interior designers need to exploit in their business service model. The lighting and emergency simulations were constructive collaborations between me and two talented graduate students. At the time, we were pushing the boundaries of what virtual environments were and how they could contribute to building a scholarly knowledge base for the discipline of interior design. My most recent investigation – the millennial study – brings my academic journey full circle by asking how to best respond to the challenges of teaching creatively with technology. The question this time is driven less by new software applications and hardware availability than by managing social networking. I remain passionate about what can be learned through the examination of virtual interiors and I look forward to reimagining interior design pedagogy with a studio filled with millennial students.