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Teaching innovative design reasoning: How concept–knowledge theory can help overcome fixation effects

ARMAND HATCHUEL, PASCAL LE MASSON, AND BENOIT WEILCGS Center for Management Science, MINES ParisTech, Paris, France

(RECEIVED April 15, 2009; ACCEPTED September 9, 2010)

Abstract

How can we prepare engineering students to work collectively on innovative design issues, involving ill-defined, “wicked”problems? Recent works have emphasized the need for students to learn to combine divergent and convergent thinking in acollaborative, controlled manner. From this perspective, teaching must help them overcome four types of obstacles or “fixa-tion effects” (FEs) that are found in the generation of alternatives, knowledge acquisition, collaborative creativity, and crea-tivity processes. We begin by showing that teaching based on concept–knowledge (C-K) theory can help to manage FEsbecause it helps to clarify them and then to overcome them by providing means of action. We show that C-K theory canprovide scaffolding to improve project-based learning (PBL), in what we call project-based critical learning (PBCL).PBCL helps students be critical and give due thought to the main issues in innovative design education: FEs. We illustratethe PBCL process with several cases and show precisely where the FEs appear and how students are able to overcome them.We conclude by discussing two main criteria of any teaching method, both of which are usually difficult to address in sit-uations of innovative design teaching. First, can the method be evaluated? Second, is the chosen case “realistic” enough?We show that C-K-based PBCL can be rigorously evaluated by teachers, and we discuss the circumstances in which a C-K-based PBCL may or may not be realistic.

Keywords: Concept–Knowledge Design Theory; Creative Design Teaching; Project-Based Learning

1. INTRODUCTION

There is a growing need today for innovation and creative en-gineering. Increasing the pace of innovation is no longerenough. Companies try to routinely provide radical, disrup-tive innovation, and to strengthen their innovative design pro-cesses. We begin with some examples of the “briefs” given toengineers and designers in companies today:

† “Smart grids” in energy: When new energy sourcesemerge, when there is rising concern for sustainable de-velopment and CO2 emissions, and when houses andcars become power suppliers, how do engineers designsystems and services for energy production, transport,and distribution?

† Home networking in information technology: Whenhomes becomes nodes for multiple information net-works (TV, cell phones, radio, wi-fi, cable, etc.) formultiple, often emerging, uses (communication, music,

photographs, movies, personal data exchanges on socialnetworks, remote working, etc.), how do engineers de-sign the services and products for the emerging businessmodels and the value chain of information technologycompanies, Internet access providers, software andhardware providers, and so forth?

† New urban mobility: When cities want powerful but low-cost public transport systems, when bus transport sys-tems become as effective as metro systems (e.g., theBus Rapid Transit concept), and when bikes or cars be-come means of public transport (e.g., Velib public bikesin Paris), how do engineers design services for mobility?

In such situations, engineers are actually in charge of de-signing new functional spaces (and not only meeting clearlyspecified requirements), producing new competencies (andnot only using existing skills), and designing new businessmodels (and not only following customer requirements andsuppliers’ constraints). This has to be done collectively,with increased interactions with industrial designers, archi-tects, scientists, and users in complex institutional contexts(far from the classical relationship designed to “meet the cus-tomers’ demands”).

Reprint requests to: Pascal Le Masson, CGS Center for Management Sci-ence, MINES ParisTech, 60 Boulevard Saint Michel, F-75 272 Paris Cedex06, France. E-mail: lemasson@ensmp.fr

Artificial Intelligence for Engineering Design, Analysis and Manufacturing (2011), 25, 77–92.# Cambridge University Press, 2011 0890-0604/11 $25.00doi:10.1017/S089006041000048X

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Engineers have to modify their design reasoning instead ofsimply applying their competencies in the engineering sci-ences. They also have to collaborate with other knowledgecreators. How can we prepare engineers for such innovativedesign issues?

First, we will review the elements that engineers have tolearn to be prepared for so-called innovative design situa-tions. Second, we will show that a new design theory, suchas concept–knowledge (C-K) theory, can help teach creativedesign in what can be called a project-based critical learning(PBCL) process, that is, project-based learning (PBL), scaf-folded by a theory. Third, we will illustrate C-K-basedPBCL with two teaching cases.

We must point out that this paper strictly addresses the is-sue of innovative design. Classical rule-based design is nottreated here and the proposed method, C-K-based PBCL, inno way intends to address the issue of rule-based design.

2. WHAT MUST BE LEARNED OR TAUGHT: THECRITICAL ISSUE OF OVERCOMING ALLFACETS OF FIXATION EFFECTS (FEs)

This section will show that innovative design teaching actuallyprepares learners to deal with four obstacles: FEs in the gen-eration of alternatives, FEs in knowledge acquisition, FEs incollaborative creativity, and FEs in the creativity process.

2.1. Main issues in teaching creative engineering

What is at stake in creative engineering is the way engineersdeal with problems such as those described above. These prob-lems are usually characterized as “ill-defined” (Simon, 1969)or “wicked” problems (Rittel & Webber, 1972; Dunne & Mar-tin, 2006) with “figural complexity” (Schon, 1990). With suchproblems, engineers’ traditional methods of reasoning are notenough as the situation is radically different from classical op-timization and modeling. In such situations, engineers havealso to collaborate with other designers, such as industrial de-signers or architects, who also reason in a very different wayfrom optimizing and modeling. Hence, the issue for teachingis to help engineers acquire this capacity for innovative designreasoning. This requires more than just adding a new scienceto the engineering sciences (McMahon et al., 2003).

It is interesting that the history of engineering has alreadybeen through periods where new forms of innovation have re-quired major changes in teaching: the first industrial revolu-tion forced engineers to be able to deal with complex ma-chines for various applications, and this led to the inventionof parametric design at the German Technische Hochschule(Redtenbacher, 1852; Konig, 1998). The second industrial re-volution forced engineers to deal with “science-based pro-ducts” (electrical engineering, chemical engineering, etc.),and this led to the invention of systematic design (Rode-nacker, 1970; Pahl & Beitz, 1977; Heymann, 2005).

What issues are involved in teaching innovative design?Following Dym et al. (2005), they can be summarized as

teaching divergent thinking (DT) and convergent thinking(CT) in a collaborative, controlled manner. First, teachingDT consists in making people able to formulate original pro-positions. As observed by Loch et al. (2006), this is muchmore than being able to act in uncertain situations. In uncer-tain situations, alternatives are known and only their probabil-ity is unknown, whereas DT consists in acting in situationswhere the nature of the alternative is unknown, where new,original worlds have to be created. This creation requiresthe capacity to break existing generative rules (Boden,1990) and to create alternatives. It is also accepted that de-signers should not only be able to use existing knowledgebut also have to ask so-called “generative design questions”(Eris, 2003, 2004).

Second, teaching CT does not consist in teaching the engi-neering sciences, but rather in teaching the capacity to useknowledge, for instance through “deep reasoning questions,”that is, with a capacity to activate expertise, to transform itinto usable skills, and to be able to link existing, abstract en-gineering science models to so-called “hardware” situations(Brereton, 1999). This is different from the capacity to opti-mize and is closer, for instance to the capacity to formulaterelevant “estimates” (Linder, 1999; Dym et al., 2005), thatis, knowledge relevant for a particular situation.

Third, teaching DT and CT (DTCT) also aims at makingdesigners able to switch regularly from one mode into theother (Eris, 2004; Dym et al., 2005). Several works have em-phasized interesting challenges of this DTCT design process.The process of iteration should be cumulative and lead tolanguage expansion (Mabogunje & Liefer, 1997). Iterationsshould keep cyclical semantic coherence (Song et al., 2003).This kind of process could be evaluated by using creativitymetrics suggested by Shah et al. (2003): a DTCT processshould lead to originality and variety but also to robust, feasi-ble solutions.

Fourth, designers, especially engineers, have to be able tofollow an innovative design process in a collaborative manner.One goal is therefore to enable them to follow DTCT processeswith users (von Hippel, 2001; Magnusson, 2003) from varieddisciplines, with different “types” of people: either differentMyers–Briggs Type Indicator profiles (Reilly et al., 2002) ordifferent design traditions (architects, industrial designers, en-gineers, etc.; Rice, 1994; Savanovic & Zeiler, 2007).

Fifth, teaching innovative design should also enable peopleto control whether or not they are following the process cor-rectly. This criteria of controllability is mentioned regularlyin design teaching (Pahl & Beitz, 2006). It was theorizedby Argyris and Schon (Schon, 1983; Argyris & Schon,1996) in the notion of double-loop learning, in which design-ers have to be able not only to use the “espoused theory” butalso to change it to identify new ways of doing things in theso-called theory in use. In the case of DTCT, people shouldbe taught to control when they should switch from DT toCT or from CT to DT. They should learn to control the bal-ance between creative thinking and resources for exploringand learning, and they should be able to control whether their

A. Hatchuel et al.78

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group of designers is in a design process and the quality oftheir work at all times.

The literature provides us with several insights into thegoals of teaching innovative design. It also identifies themain questions for teaching. First, how do we evaluate the ca-pacity for DT? As observed by Dym et al. (2005), it is diffi-cult to design and grade an exam to test whether students areable to formulate undecidable propositions: “how to evaluatesomething that is neither true nor false?” Second, how do weteach the balance between DT and CT? It is possible to teachmethods of inquiry and creativity, but how can they be com-bined? Third, how can the “process” be evaluated? Innovativedesign usually tends to be taught with a “PBL” model (seeDym et al., 2005). The teaching aims precisely to help stu-dents learn to design in a creative, collective, and controllableway. However, as observed by Dym et al. (2005), this raises anumber of questions. What is an “authentic situation”? Teach-ing, and even more so evaluation, should concern the processitself and not merely the output. How do we evaluate whetherthe process is under control? How do we ensure that the teamis varied enough? How do we check the capacity to designcollectively? It is not by forcing people to work together inmultidisciplinary teams that their collective work is necessar-ily effective in terms of innovative design?

2.2. Analyzing the obstacles encountered by learners:Four forms of FE

Having outlined the main issues and the questions they raisefor teaching, we must try to gain a clearer picture of the mainobstacles that designers meet when learning innovative de-sign. Some of the requirements of innovative design maybe met by natural aptitude, others through regular teaching.However, recent works on cognition and psychology helpto clarify some obstacles that apparently almost everybodyis likely to face in innovative design situations. In the litera-ture we have identified four main obstacles that can be char-acterized as four types of FE: FEs in the generation of alter-natives, FEs in knowledge acquisition, FEs in collaborativecreativity, and FEs in creativity processes. We will now clar-ify these four main obstacles in a view to establishing a frame-work for analyzing innovative design teaching methods,which should help overcome at least one of these FEs.

1. Works on creative cognition1 have analyzed the cog-nitive factors that can limit creativity (Ward et al.,1999). They show that the number of ideas is not neces-sarily the main issue, although it is often used as an es-timation of creativity. However, the ability to meet thecriterion of “variety” is a major issue. A first series ofworks showed that people always tend to generate ideasin the same “family,” showing “fluency” but limited

variety. This was described for instance in Ward’s ex-periments (Ward, 1994) that show how people in creativeexercises tend to follow the “path of least resistance”:when told to draw imaginary animals, people tend todraw animals with legs, heads, and eyes. Originality isalso limited in creative experiments. Although peopleare expected to break the rules (Boden, 1990), that is,to rediscuss the knowledge they have, they tend ratherto reuse knowledge and, more specifically, to reuse re-cently activated knowledge (Jansson & Smith, 1991;Smith et al., 1993). A first criterion for teaching innova-tive design therefore emerges here: it should help to over-come the FE in the “generation of alternatives.”

2. These works also help identify a second cognitive factorthat limits creative cognition: people have difficulty mak-ing an effort to learn during a creative process, for in-stance by observing uses or by making “crazy proto-types.” Although it has been shown that learning,modeling, scanning or, more generally speaking, compe-tence building, greatly accelerates creativity (Alexander,1964; Sutton & Hargadon, 1996; Cropley, 2006), thereis a tendency to focus on existing knowledge or to usethe knowledge specific to the goal and task to be met(Finke, 1990). The issue is not that designers learn to pro-duce knowledge, but that they produce knowledge that isused in the design reasoning. This leads us to suggest asecond criterion for innovative design teaching: it shouldhelp to overcome the “K acquisition” FE.

3. Another stream of works has emphasized the cognitiveand psychological difficulties involved in collaborativecreativity. It is well known that groups using brain-storming methods (Osborn, 1957) generate signifi-cantly fewer ideas than the combined total of ideas gen-erated by the same number of individuals brainstormingalone (called the nominal group; Diehl & Stroebe,1987; Mullen et al., 1991; Paulus & Dzindolet, 1993).Several research projects have studied this “productivitygap,” providing evidence of a FE in “collective creativ-ity” in brainstorming. Various social causes of produc-tion blocking have been identified, such as divided atten-tion (Mulligan & Hartman, 1996; Paulus & Yang, 2000),social pressure of ex post evaluation (social anxiousness;Camacho & Paulus, 1995), the effect of perceived expert-ness on creativity (Collaros & Anderson, 1969), and alack of recognition causing loafing and free riding. Cog-nitive factors have attracted renewed interest in recentyears, with a focus on the risk of similarity in idea asso-ciations: the idea associations tend to follow the rule ofsimilarity, meaning that the ideas generated from oneidea tend to be in the same category as the initial idea(Brown et al., 1998; Paulus et al., 1999, 2000. This iscoherent with the individual FE reported by cognitivepsychologists (Ward et al., 1999) mentioned above.Moreover, unique ideas have poor association value:they initiate fewer ideas because the knowledge requiredfor generation is not shared by the participants (Stasser &

1 We used the term creative cognition in the classical sense: a trend in psy-chology that tries to understand creative processes by using methods and con-cepts of cognitive sciences.

Teaching innovative design reasoning with C-K theory 79

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Birchmeier, 2003). Other works have insisted on the riskof not fully exploring the full range of ideas (Gigone &Hastie, 1997; Paulus, 2000; Stewart & Stasser, 1995)and the risk of cognitive load (each individual followshis or her own idea generation process while followingthe collective exchanges). Finally, there have been studiesof phenomena regarding the limitations of knowledge ex-pansion, such as the convergence of a group of peopledrawing on a common knowledge base (Stewart & Stas-ser, 1995). Such phenomena illustrate a “collaborativecreativity” FE and help identify a third criteria for innova-tive design teaching: it should help people to overcomethis effect by “creatively” building on the others’ ideasand using the knowledge provided by them.

4. Finally, an FE has been reported in the process itself:when studying DTCT processes, it appears that designerstend to organize a two-step process (Eris, 2004; Plety &Cremet, 2007; Shah et al., 2003), with an initial period ofdivergence in which a single “good” idea is usually se-lected and then developed during the convergence pro-cess. Students in design processes tend to identify a firstphase of problem framing (or problem setting) and a sec-ond phase of problem solving. Schon’s description of the“reflective practitioner” (Schon, 1983) and Sutton andHargadon’s (1996) description of IDEO creative pro-cesses show processes where designers are able to shiftregularly from DT to CTand from CT to DT. A fourth cri-teria for teaching innovative design therefore emerges: itshould help to overcome the “creativity process” FE.

It is interesting to note that history has provided us withseveral methods for overcoming some of the FEs listedhere. We can illustrate how a teaching method overcomesone of these FEs with a few examples. For instance, one strik-ing example in industrial design tradition is the educationalprogram of the Bauhaus school (Droste, 2002). It was basedon powerful theoretical works by Gropius, Itten, and others.In his introductory course, Itten (1975) did not follow theclassical teaching pattern of the Beaux Arts (i.e., copy themodels) but taught “the fundamental laws of colours, forms,composition and creation” (p. 31). Students then had to dothree types of projects: studies of nature and materials, anal-yses of old masters (such as the Issenheim Altarpiece), andnudes. He taught a grammar of shapes, colors, contrasts,rhythms, and materials, showing the different materials’ es-sential and contradictory aspects.

Engineering design has also done a great deal of work onteaching innovative design. One archetype reported by Konig(1999) is Peter Klimentitsch von Engelmeyer (1855–1939), aRussian–German engineering design professor and theoreti-cian who proposed the first integrated engineering design the-ory that linked intuition and knowledge creation into a designprocess. Klimentisch built a “Theorie der kreativen Arbeit”(1912; see also Engelmeyer, 1895) and deduced from it ascaffolded process of PBL. He defined three types of projects,which can be characterized by different levels of expansion:

designing a variant of an existing machine (a computing prob-lem only), improving a function of a machine (applying sci-entific knowledge when the main working principles areknown), and new construction (an Edison-like project, requir-ing the investigation of new physical principles to addressemerging needs).

Even if they address different types of designers, both teach-ing processes share common principles: they insist on knowl-edge acquisition for creative design (grammar of shapes, studyof old masters, reverse engineering, etc.); they underline thelimits of existing knowledge (exercises to explore new combi-nations of shapes, colors, and materials, projects to improvemachines or even to explore new phenomena, etc.) and theytrain students to face unknown design situations (Bauhausteaching program, Klimentitsch’s innovative projects). In thisway, they address the FEs in “the generation of alternatives”and in “knowledge acquisition.”

To summarize, our literature review has helped to build aframework for the main criteria used to evaluate innovativedesign teaching methods. Such a method should meet thefollowing four criteria:

1. Overcome FEs in “the generation of alternatives”: Themethod should help to generate “varied” and “original”alternatives.

2. Overcome FEs in “knowledge acquisition”: The methodshould help to generate and acquire relevant knowledge.

3. Overcome FEs in “collaborative creativity”: The methodshould help to use the other actors’ knowledge and buildon their ideas.

4. Overcome FEs in the “creativity process”: The methodshould help to combine problem setting and problemsolving in a nonlinear process.

This analysis of the FEs encountered by students learninginnovative design helps to characterize the issue of teaching:classical methods of “teaching” creativity usually control theoutput of the creative process. This control is grounded on cri-teria derived from Guilford or Torrance criteria, taking intoaccount specific aspects of engineering (Guilford, 1950,1985; Torrance, 1988; Shah et al., 2003). But the above-men-tioned analyses of collective creative processes show that thefinal performance is actually impeded by FEs: hence, the is-sue is to devise a teaching method that helps to control andmanage this “process” variable, which is often widely uncon-trolled. In this perspective, the students are not only be eval-uated on the outputs but also on the process: the evaluationshould control the students’ capacity to control and overcomethe FEs during the innovative design process.

3. C-K BASED TEACHING TO OVERCOME FES

We will now use two approaches to show that a C-K basedmethod can precisely help students to overcome all four FEs.In the first, we show that the method can intrinsically overcomethe four FEs because it actually enables the learner to clarify

A. Hatchuel et al.80

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and control the FEs. The second approach consists in casestudies illustrating how students learn by working with C-K.

Note that the aim is to prove that the method helps to over-come a FE. However tempting, it must be pointed out that acomparative experiment comparing the ideas generated withand without the teaching method does not necessarily controlthe FE, because it is difficult to know whether the occasionaldifference results from a capacity to break rules, better knowl-edge acquisition, a capacity to use knowledge, and ideas pro-vided by the others and/or a refined process. We are lookingfor a method that actually helps to explain the design rulesthat have been broken, the knowledge produced during theprocess, the knowledge and ideas provided by each of the ac-tors, and the process employed. We will see that the C-K the-ory can be used to do so, as the advantage of the method isprecisely that it helps the designer to become aware of hisown FEs and then offers him the means to overcome them.We have therefore favored a formal approach and case studiesto show how C-K controls the FEs.

3.1. Elements of C-K theory

C-K theory (Hatchuel & Weil, 2003, 2007) was initially de-veloped to support innovative design teaching. We will nowsee how it meets the requirements of an innovative designtheory. Defining K as the space containing all established(true) propositions (the available knowledge), a C-K designprocess begins with a proposition that is undecidable in K(neither true nor false in K) about some partially unknownobjects x. Concepts are all of the following form: “There ex-ists an object x, for which a group of properties P1, P2, Pkhold in K.”

Concepts can only be partitioned or included, not searchedor explored. If we add new properties (K! C), we partition

the set into subsets; if we subtract properties we include theset in a set that contains it. Nothing else can be done. Afterpartitioning or inclusion, concepts may still remain concepts(C ! C), or move to propositions of K (C ! K). The twospaces and four operators (including the K! K) are shownin F1Figure 1.

Any design project intends to transform an undecidableproposition (concept)—its “brief”—into a true propositionof K by adding new properties to C coming from the spaceof knowledge K and by producing new knowledge guidedby conceptual issues. These partitions of the concepts canbe either restrictive or expansive. If the partition expandsthe definition of an object with a new property, it is calledan expanding partition (e.g., a flying house). Conversely, ifthe partition relies on an existing definition of the object, itis called a restrictive partition (speaking of “a house with ared roof” is a restrictive partition if “houses with red roofs”are already known in K).

Creativity is the result of expansive partitions of concepts(Hatchuel et al., 2008). Another view of C-K dynamics isgiven in F2Figure 2. Because of the partitioning process, theC-structure is necessary as a tree structure, whereas the struc-ture in K could be completely different. We also see in thispicture that any expansion in C is dependent on K and viceversa. Any choice to expand or not to expand in C is K depen-dent. Design begins with a disjunction and will only end if aconjunction exists and is judged as an acceptable solution.

As in the Bauhaus and Klimentitsch cases, C-K theorycombines the classical engineering design emphasis onknowledge and knowledge creation with the requirementfor creativity to venture into the unknown (C0) and breakthe (right) rules to create new, original artifacts (expandingpartition; Boden, 1990; Le Masson et al., 2007; Hatchuelet al., 2008).

Fig. 1. The four operators of concept–knowledge theory (Hatchuel & Weil, 2003). C, concept; K, knowledge. [A color version of thisfigure can be viewed online at journals.cambridge.org/aie]

Teaching innovative design reasoning with C-K theory 81

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3.2. How C-K helps to control and overcome FEs

Let us now see how C-K addresses the four issues of designteaching identified above. We show that the theory helps toteach students in two ways: it clarifies the performance tobe achieved and the means of action to achieve it and, morespecifically, it enables students to clarify what “fixes” themand to make sure that they overcome the problem.

3.2.1. C-K and FEs in the generation of alternatives

In C-K, the FE in the generation of alternatives can be mod-elled as a limitation of C expansion because of the preserva-tion of a stable K structure, with a stable set of generativerules in K (seeF3 Fig. 3).

The operators of C-K, described above, directly enable thegeneration of design paths. More precisely, it is possible toevaluate “variety” by analyzing the structure of C and thevariety of K bases that students mobilize for generating de-sign paths. It is also possible to check whether or not the ideasare original. Following the definition of “originality” givenby Boden (1990), an idea is original if it breaks a designrule; this corresponds to an expansive partition in C-K(Hatchuel et al., 2008). Hence, we see how C-K theory canhelp students to generate alternatives and to control whetherthese alternatives are varied (number of partitions and varietyof knowledge bases mobilized in these partitions) and origi-nal (number of expansive partitions).

Moreover, C-K intrinsically allows for a control on the FEsin idea generation: C-K process helps identify and position allthe “classical ideas” generated by using “common knowl-edge” and classical “generative rules”; this common knowl-edge and these generative rules must be shown in the space

K. C-K therefore clarifies what fixes the designer; it also of-fers a self-evident way of overcoming this type of FE: as soonas a “classical generative rule” is identified, the student can“break” it, that is, can generate a concept in C that negatesthe generative rule.

For instance, in anticipation of the illustrations below (Sec-tion 4), when students are told to design a “smart shoppingcart” using C-K theory, they identify that one generativerule is “a physical cart containing the goods the shopper wantsto buy” (written in K). Hence, K-base contains a classical de-sign rule. Immediately, they can break this rule by writing inC: “a smart shopping cart that is a physical shopping cart thatdoesn’t contain the goods.” Building on this idea, they will goback and forth between C and K to finally expand the smartshopping cart into “a scanner that only ‘selects’ the items, ina supermarket that is redesigned as a showroom.”

3.2.2. C-K and the FEs in “knowledge acquisition”

In C-K theory, the FEs in knowledge acquisition appear asa limitation of the knowledge expansion (see Fig. 3).

In a formal sense, a design student using all four operatorsis supposed to produce knowledge (K ! K) and to use thenew knowledge in C. It is self-evident that C-K helps to over-come the “knowledge acquisition” FE as students have onlyto check that they acquire knowledge and use it during theprocess; if they only use knowledge they had at the beginningof the process, this implies that there is an FE. To control thistype of FE it is therefore important to begin with a careful, de-tailed state of the art review.

This formal approach can be completed by some guide-lines on knowledge acquisition. For instance, students can

Fig. 2. The concept–knowledge dynamics (Hatchuel et al., 2004). [A color version of this figure can be viewed online at journals.cam-bridge.org/aie]

A. Hatchuel et al.82

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check that they actually acquire knowledge on feasibility anduser value. This knowledge should first be shown in K. How-ever this “acquisition” is not enough: the design studentshould also check that this knowledge is actually used inthe reasoning in C. This means that partitions should appearin C, based on the emerging feasibility and value criteria.Note that this does not mean that the student has validatedthe criteria; the student simply makes partitions based onthe emerging value and robustness criteria. Hence, C-K the-ory helps students to acquire knowledge on robustness andthe value of the concepts they work on.

Let us take an example from the smart shopping cart case.When working on a smart shopping cart displaying informa-

tion to the shopper, students discovered that the shopping cartalso has to be extremely robust, reliable, and as cheap as pos-sible. A “feasibility” criterion in K thus emerged. This knowl-edge was interesting when fed back into the reasoning to givethe concept “a shopping cart with a display, which is still fea-sible, at a low cost.” Working on this concept, students lookedfor available displays and discovered that a large majority ofusers already have a display device in their pockets! This fi-nally helped identify a concept of a shopping cart with a plugto enable shoppers to use the display on their own mobilephones.

Note that the robustness and value criteria depend on thedesign path: two different paths can lead to very contrasted

Fig. 3. A representation of the fixation effects in the generation of alternatives and in knowledge acquisition. C, concept; K, knowledge. [Acolor version of this figure can be viewed online at journals.cambridge.org/aie]

Fig. 4. A representation of the fixation effects in collective creativity and creativity processes. C, concept; K, knowledge. [A color versionof this figure can be viewed online at journals.cambridge.org/aie]

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value and robustness criteria. For instance, the “show roomscanner” will lead to value criteria on the type of “shows”to be organized: the value could come from the user tastingthe products and discovering new combinations of products(ingredients of a meal). The “universal plug shopping cart”will lead to explorations of value criteria on “what could bedisplayed,” “who would be interested by displaying some-thing to the shopper,” and so forth.

3.2.3. C-K and the FEs in “collective creativity”

In C-K, the FEs in collective creativity appear in severalways. In a group, the C-K diagram of the group will tend toonly represent the common knowledge base; consequently,the concepts will only be based on this knowledge (see

F4 Fig. 4). Seen from the point of view of one member of thegroup, this observation means that the student does not useknowledge that he did not have beforehand, as it was pro-vided by another student of the group; similarly the studentis unable to provide knowledge on a concept proposed bysomeone else. The group maintains a common knowledgebase in K, that is, everybody uses this knowledge base, andconcepts that could lead to the expansion or rediscussion ofthis knowledge base are neglected by the group.

C-K not only helps to make the “collective creativity” FEvisible, but also helps to overcome it in several ways. First,the C-K diagram helps to provide an overview of the group’sreasoning. By clarifying the design paths and the availableknowledge bases, it helps students to make use of the knowl-edge provided by others or to provide knowledge on conceptsproposed by others. Second, it becomes possible to give sim-ple guidelines to create ideas that could only emerge in agroup: for instance, ask individual students to identify a pieceof knowledge that is new to them and to make use of it in oneof their concepts and, conversely, ask them to provide onepiece of knowledge on a concept that is new to them.

It can also help to make heterogeneous designers work to-gether while still respecting the variety of their talents. Forinstance, C-experts and K-experts can be identified. To sim-plify, industrial designers are more likely to be C-experts,whereas engineers are more likely to be K-experts. The col-lective creativity FE would predict that engineers wouldhave difficulty explaining and setting out their knowledgeand designers would refrain themselves from breaking designrules. By clarifying the contributions of C-experts and K-ex-perts, C-K helps them to collaborate.

3.2.4. C-K and the FEs in the “creativity process”

In C-K, the FE in the creativity process appears as a groupdifficulty to stay in C and structure the C space. Hence, C-space exploration appears as a collection of “ideas,” that is,concepts loosely connected to the main C (see Fig. 4).

C-K clarifies the series of operators, thereby offering a de-tailed account of the design process, which is more complexbut also more sophisticated than the DTCT pattern. For in-stance, C-K helps to account for the exploration of “crazyconcepts” (C inquiries), for the production of knowledge on

users or on new phenomena (K inquiries). In particular, itcan account for a variety of prototypes at the interface be-tween C and K (Edelman et al., 2008). It can also supportcomplex work division: one subgroup can explore a K base(make a study on costs or users), another can explore one C(i.e., a “quick and smart” version of the concept, or what ap-pears to be a “niche” application, etc.). For instance, in thesmart shopping cart case we found one subgroup that focusedon the “airport smart shopping cart,” not because theythought that it was the “best idea” but because it seemed tobe a promising, complementary way to explore the smartshopping cart concept.

Hence, C-K enables complex processes that go far beyondthe classical pattern of “diverging to generate ideas, selectingthe “good one” and then developing it into a feasible, market-able product.” It also helps to design without being fixedby the “problem setting–problem solving” pattern: in C-K,the design process begins with a concept, which is not neces-sarily a “problem”; this leads to a tree of concepts that are nei-ther “good” nor “bad” ideas but that together build a designstrategy.

3.3. Remarks on PBCL

When it comes to design teaching, and more precisely to in-novative design teaching, PBL is often advocated as a meansof learning about types of action. C-K-based learning mightseem to be a distinct approach, but actually it merely makessome implicit hypotheses of the PBL approach more explicit.Recent debates on PBL (Kirschner et al., 2006; Hmelo-Silveret al., 2007) show that PBL is a “scaffolded” process, relyingon expert guidance, based on “particular reasoning strategies”(Hmelo-Silver et al., 2007). Hence, teaching innovative de-sign in PBL requires a better understanding of design think-ing (Dym et al., 2005). A theory of design thinking is ex-tremely useful for design teaching, because it can be taughtand learned in a relatively short time, in controllable pro-cesses, with evaluation and exercises to improve creative ef-ficiency.

A design theory provides a means of organizing the learn-ing process and orienting it toward the most critical points tobe learned. Based on a design theory, it is possible to organizewhat we propose to call PBCL, which consists of teachinga design theory that can be related to critical cognitive andorganizational issues and organizing, on this theoreticalbackbone, a curriculum that encompasses classical teaching(i.e., the disciplinary content in engineering science) andprojects.

This process combines the advantages of classical PBL(collective experience of ventures into the unknown, motiva-tion, real-life or quasi real-life situations, etc.) and the advan-tages of a theoretical approach (offering an integrated frame-work, supporting the discovery of complex and nonintuitivereasoning, avoiding student manipulation by enabling discus-sion and criticism of the process, etc.). It should be noted thatBauhaus’ teaching, Peter Klimentitsch von Englemeyer’s

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teaching or, more generally speaking, “engineering designteaching” (Erkens, 1928; Pahl & Beitz, 1977), were actuallya combination of theoretical teaching and PBL.

In a more general sense, it seems logical to base the learn-ing concerning an action (learning to design) on the action it-self (learning based on design projects), but learning aboutaction can also be scaffolded by a theory. For instance, learn-ing to act in uncertainty can be done by playing lotto; but thislearning can also be enhanced by teaching probability theoryor teaching statistical decision-making theory (Raıffa, 1968;Savage, 1972). What is the advantage of teaching a theoryof the action? At the very least, it can help students realizethat they think differently and do not approach uncertaintyas predicted by probability theory. Students might also under-stand that probability theory performs better than humancommon sense in some uncertain situations. In both cases,the existence of a precise, rigorous theory offers students anincreased capacity for reflexivity to face risk. Let us examinethe advantages of decision-making theory:

1. It increases the understanding of the “quality” of theoutputs (what is a “good” decision? A decision madeon a clear set of alternatives with well-identified criteriaor, technically speaking, utility functions).

2. It increases the means for action (clarify the value ofnew information, build subjective probabilities, etc.).

3. It supports reflexivity (clarify counterintuitive notions,avoid the intuition traps; i.e., clarify the hazard decisiontree and avoid the intuitive but misleading representa-tion of “hazard decision”).

When describing the advantages of C-K in creative design,we found precisely the same types of advantages: a “good”innovative design is a design that has expansive partitionsand varied alternatives, based on robustness and value cri-teria; “breaking the rules” and “learning” can be consideredas essential means of action; students should be attentive toall kinds of FEs, from the “path of least resistance” to diffi-culty in staying in C and in structuring C without choosingand validating.

4. ILLUSTRATION OF TEACHING CREATIVEDESIGN WITH C-K THEORY

C-K theory has been used in several educational situations.We will discuss two types of educational case studies here,in which C-K theory was used in two different ways:

1. Type 1 case studies: C-K theory was used as a theoreti-cal framework to analyze, study, and interpret a creativeproject carried out by students who were not trained inC-K theory, which was conducted by Plety and Cremet(2007). This experiment was done on the brief “makeemployees feel less stressed when they come to work,to make them more creative.” Other studies were donewith engineering students at ENSAM and industrial de-

sign students at Strate College, on the brief “a smartshopping cart.” These case studies served to representin C-K a “pattern” of creative engineering, without acontrol of the FEs.

2. Type 2 case studies: C-K theory was used as a guidingmethod in a PBCL process to enhance the creative andinnovative power of a team, which was conducted byHatchuel and Weil (2007). These case studies werecarried out with students (Strate College for industrialdesign students; engineering students at MINES Paris-Tech, ENSAM, and Ecole Polytechnique). All of thesecase studies had the same brief: “a smart shopping cart.”

We briefly describe these case studies and indicate how thefirst case studies help to describe FEs in C-K and how the sec-ond case studies show how C-K theory helps to control andovercome the four types of FEs in collective creative engi-neering. They show how C-K theory can be a revealing ana-lytical tool and a powerful method for acting as a creative de-signer without any special insistence on being “creative”!

We conclude by discussing the evaluation of this type ofPBL and its relevance.

4.1. Type 1 case studies: Revealing the FEs in creativeprojects

This case study, named Artem, was conducted as a joint pro-gram for an art school (Ecole nationale superieure d’art deNancy), an engineering school (Ecole nationale superieuredes mines de Nancy) and a business school (Ecole de man-agement de Nancy). Over a period of 1 year, groups of fourto eight students from the three schools were asked to carryout various types of innovative projects. To assess the educa-tional and creative aspects of the projects, six of them werestudied by an educational psychologist, Robert Plety, and aprofessor of engineering. The research method used variousempirical materials including video recording of the students’project meetings. C-K theory was selected by the team as apotential framework to assess the “creative” aspects of thestudents. The researchers received a short oral presentationof the theory and had access to the main papers about it.The authors of this paper had no contact with the Artem pro-jects and only became acquainted with the findings throughthe research reports (Plety & Cremet, 2007). The main find-ings are as follows:

† Combining design and creativity in the projects: Pro-jects were initiated by a “brief” from a company, whichleft full freedom to the students. The integration of bothdesign and creativity was completely natural to the stu-dents.

† Concept formation and expansion: the students tendedto call “concept,” not the first design brief, but the “fea-sible project goal,” derived from the brief agreed uponby the group after the first intensive discussions. Hence,the role of space C was entirely implicit and appeared in

Teaching innovative design reasoning with C-K theory 85

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the form of intense work on the elaboration of the “con-cept.” As a result, there was no formal building of a setof concept variants with several degrees of elaboration.Nonetheless, the notion of concept expansion describesthe students’ activity very well.

† Knowledge activation and generation: The develop-ment of the concept was obtained through a knowledgeprocess where both activation and generation of newknowledge were intensive. Students had to go far be-yond their own skills and managed several inquiries inareas that they had known nothing of beforehand. More-over, the positive relationship between the expandingpower of the concept and the intensity of the new knowl-edge generated was clearly observed see also Mabo-gunje and Leifer (1997). It is interesting that this knowl-edge acquisition process is often driven by the “feasibleproject goal.”

† Two distinct project phases–Co-elaboration and coop-eration: The project seemed to follow a linear sequenceof two phases that were not linear themselves.

1. The first phase was described as “co-elaboration” byPlety and Cremet (2007) and clearly corresponds tothe period of intensive discussion, knowledge expan-sion and the generation of the “concept.” It can beinterpreted as the phase of creative design.

2. The second phase was described as “cooperation.” Itcorresponds to the gradual elaboration of the concept,but Plety and Cremet (2007) insist on the fact that thework division in this phase closely followed the stu-dents’ curricula: engineering students behaved as en-gineers, art students as artists, and business studentsas managers. Thus, the project was more like a devel-opment program. It was also clearly observed thatonly during the creative design phase did all studentsbehave very similarly, and it was difficult to recognizewho was studying which curricula. It was as if thelogic of creative design were universal and commonto all the professional traditions.

† Informal conceptual expansion: Case study 1 stronglysupports the fact that “spontaneous” creative design isimpeded by FEs: students tended to reduce, oversim-plify or neglect the structuring of C, had difficulty ingenerating knowledge for “idea generation” and pre-ferred to produce knowledge to realize the “feasible pro-ject goal.” The collective work focused on a single, con-sensual “solution” and did not divide up the explorationwork. Finally, the group spontaneously divided thework into two phases, corresponding to the classicalDT and CT phases.

Other case studies support this first analysis. Severalgroups of students, from two design schools (the Strate Col-lege industrial design school and the ENSAM engineeringschool) were asked to propose creative alternatives for a smartshopping cart. They had access to Internet. They had received

no previous education in C-K. The students presented theirresults as a list of proposals, each proposal being more orless detailed with illustrations and technical and/or functionaldetails. We authors have presented the proposals in the C-Kframework to identify the concepts and the knowledge usedto produce the concepts, giving the following archetypalpattern (see F5Fig. 5).

This pattern illustrates the different forms of FEs:

1. FE in the generation of alternatives: The divergence oc-curs as a first series of ideas; then the idea generationfocuses on one main aspect (in this case, smart move-ment); finally, the idea generation process stops withthe selection of one main idea that is then developedinto a feasible product.

2. FE in knowledge acquisition: Students make use of theknowledge they have but do not use Internet in the firstphase; when the “feasible project goal,” that is, the“good” idea is selected, they use Internet to find meansof implementing the idea (in this case, energy sources,techniques and guidance principles for driving a shop-ping cart automatically into a supermarket). Hence,knowledge is acquired, but mostly “after” the diver-gence phase.

3. FE in collaborative creativity: There is no division ofwork during the generation of alternatives, when thewhole team works together; the students share commonknowledge and do not bring individual, specific knowl-edge to the group. In this process, students build on theothers’ ideas and move in the same main direction (inthis case, different alternatives for smart movement).

4. FE in creative processes: the group spontaneously orga-nizes a two-phase process. In the first phase, students“diverge” to generate several ideas; in the second phase,students select an idea and try to develop it (CT).

What would have been the impact if the students had re-ceived preliminary training in C-K theory or if C-K theoryhad been given as a normative framework for the designwork? Ideally, it would have been highly valuable to have Ar-tem groups with and without training in C-K theory, but thiswas not possible. Case Study 2 nonetheless gave us interest-ing indications of the impact of C-K theory as a prescriptiveguide to creative design.

4.2. Type 2 case studies: Controlling and overcomingthe FEs

In this second type of case study, groups of students trained inC-K theory were required to use it to design a smart shoppingcart. The only available source of new knowledge was free In-ternet access. The work was done in a very limited time of 2 has a severe test of the power of the method. The case studywas carried out eight times with groups of four studentsamong engineering design, industrial design, and manage-ment students. It is important to note that the students were

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not told to be “creative” but to try to build the greatest possi-ble expansion in both C and K. The final results consisted incomplete C-K diagrams and not just one concept or a list ofideas. This brief was chosen to follow the IDEO experimentbroadcast by ABC News in 1999 on the same brief. Thebroadcast was unknown to the students who took part in thecase study.

These workshops were led by one or two coaches (profes-sors). The role of the coach was strictly defined: they couldonly help students to apply the theory: they help to clarifywhether a student’s proposition is a concept or a piece ofknowledge, they ask for clarification of the operators usedto go from C to K and K to C, they suggest to go to Cwhen students were blocked in K and vice versa. They didnot provide any knowledge and did not formulate any C-proposition.

We start with the main observations, insisting on the con-trast with the type 1 case studies.

Use of the method—Space C case: Despite its abstract as-pect, C-K theory seemed easily accepted by the students.However, in practice, it was constantly observed that therewas a spontaneous focus on discussions in space K and ne-glect in structuring space C. The coaches had to interveneto refocus on a thorough structuring of space C. In compari-son to case study 1, it is interesting to find the same sponta-neous behavior that tends to neglect working directly on theconceptual alternatives that are precisely the source of ex-panding partitions. It is as if it were true that analogy, meta-phors and other “good ideas” come through pure serendipity.

Once asked to clearly model space C, the students were them-selves surprised by the power of the partitions generated bythe simple interplay of the mechanisms alone.

Easy generation of original ideas: The mandatory use ofthe dual expansion process systematically generated a widerange of novel and surprising ideas including examples ofrule-breaking creativity, where the notion of the “cart” wasgreatly enlarged and finally questioned in some cases farmore than in the standard creativity process described abovein the first type of case studies.

EXAMPLE 1. Connecting the cart to personal mobilephones as a display: The concept of a smart shoppingcart was repeatedly expanded by adding a new display tothe cart, which became an interface offering a large varietyof services such as information, navigation, help, and adver-tising. But rapidly, existing knowledge on such displays, onthe tough conditions suffered by carts (outdoor storage, mul-tiple shocks, loads, etc.) tended to increase the cost of the dis-play and rapidly threatened the concept. The idea was usuallyeither simplified or abandoned. This is precisely the effect ofthe definition of “the cart” in K and the idea that the display isan attribute of the cart and a poor development of C. Whenasked to use C-K completely, the students had to model allthat they knew about “displays” in K. This revealed the ob-vious fact that most shoppers already have at least one displaydevice and sometimes more in their pockets (cell phones,PDAs, etc.). Displays therefore became an attribute of theuser (expanding partition) not of the cart. Consequently, it

Fig. 5. The archetypal pattern of collective creativity without a concept–knowledge (C-K) scaffold. The white letters on a dark backgroundin K indicates knowledge expansion, that is, knowledge acquired during the process (mainly through Internet connections). [A color versionof this figure can be viewed online at journals.cambridge.org/aie]

Teaching innovative design reasoning with C-K theory 87

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was logical to divide the concept “smart shopping cart with adisplay” in C into two new concepts, in which the display be-longed to the users or to the cart. Evaluating the former con-cept, almost all the issues about the display disappeared and anew comprehensive class of interfaces between the user andthe supermarket emerged (see C-K graph inF6 Fig. 6). 0

EXAMPLE 2. Redesigning the supermarket: When stu-dents had to take into account that shopping on the Internetneeds a “virtual shopping cart” they began to build a newvariety of combinations and hybrids in C between the Internetshopping process (the shopper chooses at home and purcha-ses are delivered at home) and traditional physical supermar-ket shopping (the shopper chooses in the supermarket andbrings purchases home). In these combinations, expandingpartitions appear systematically, offering different, new iden-tities for supermarkets such as reinventing the showroomwith a cart reduced to a recording and intelligent device(the shopper chooses in the shop but the purchases are deliv-ered). Hence, the structuring of C led from a smart shoppingcart in a traditional supermarket to smart supermarkets withappropriate shopping carts! (See C-K graph inF7 Fig. 7.) 0

C-K as a systematic method for innovative design: Finally,the type 2 case studies strongly support the idea that the crea-tive process, which aims at novelty and value, corresponds toa systematic type of reasoning that is correctly captured by C-K theory. Training the students in this type of reasoning helpsavoid any reference to a strange creative process believed toproduce ideas coming from nowhere, without any clear pro-cess. In addition, students are often skeptical about the levelof novelty they can reach by themselves or through a “creative

effort” on their part. When they are convinced by the power ofC-K theory, they usually feel far more at ease when faced withinnovative projects.

These examples also illustrate how C-K serves to controland overcome the FEs:

1. FEs in the generation of alternatives: A first evaluationof the capacity to overcome such FEs consists in mea-suring variety and originality. More precisely, the C-K diagrams enable students to find the “classical” solu-tions in C (Internet shopping cart, display added to acart, etc.) and encourage them to explore alternativesto these classical alternatives. One way of doing so con-sists precisely in breaking the rules that lead to classicalinformation (see the example of breaking the rule“owned by the supermarket”). This makes it possibleto propose “strange” alternatives that are apparently“nonsensical” or, more precisely, are propositions with-out a logical status (the shopper goes to choose but doesnot bring goods home or, conversely, the shopper doesnot go to choose but brings things home!). It helps tokeep them alive without killing them too fast and, aboveall, to begin to give them a logical status (showroom).

2. FE in knowledge acquisition: A quantitative evaluationconsists in measuring robustness and value criteria (i.e.,knowledge on certain “killer tests” such as costs or re-liability) and, more precisely, the number of criteriathat were not usually associated to a shopping cart.More precisely, the C-K process makes it possible tocarry out a rigorous, enriched state of the art review, in-cluding use analysis, stakeholders’ interests (not only

Fig. 6. The first example of a concept–knowledge (C-K) diagram completed by a group of students educated in C-K. The white letters on adark background in K indicates knowledge expansion, that is, knowledge acquired during the process (mainly through Internet connec-tions); the white letters on a dark background in C indicates an expansive partition in C, that is, partitions that add unusual attributes tothe concept (e.g., the shopping cart is always provided by the supermarket, but the concept considers that part of the shopping cart isnot provided by the supermarket). [A color version of this figure can be viewed online at journals.cambridge.org/aie]

A. Hatchuel et al.88

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the shopper but also the supermarket or the goods man-ufacturer), modeling of shopping concepts (analyze asupermarket as a compromise between a logistics centreand a showroom) and provocative examples (analyzeIKEA as a separation of these two supermarkets, theshowroom on the one hand, the logistics center on theother hand). The acquisition of knowledge contributesto the generation of alternatives.

3. FEs in collaborative creativity: FEs appear when thereis a strict relationship of one participant—one con-cept—one knowledge base (often only the initial, com-mon knowledge base). Because the students wereobliged to work using the C-K method, they had to clar-ify their knowledge base and their concept, meaningthat other students were able to use the clarified knowl-edge base or to build upon the explicit concept. We con-trolled this process by following how a piece of knowl-edge (respectively a concept) was initially proposed(explored) by one student or a group of students andthen reused by another for exploring a concept (respec-tively activating another knowledge base). More gener-ally, it appears that one piece of knowledge is used forseveral concepts and one concept leads to multipleknowledge bases: such a tight, coupled pattern is asymptom of the capacity to overcome the collectivecreativity FE.

4. FEs in creativity processes: Following C-K theory, thestudents use the operators (C ! K, K ! C, K ! K,C! C) that provide a better understanding of the crea-

tive process. We see phases of “in-depth” exploration inC (refinement of a concept) or in K (clarification of aknowledge base) or “in-breadth” exploration in C (gen-eration of varied alternatives, based on a rigorous, oftenabstract model in K) or in K (generate a general modelthat helps to separate contrasted alternatives). Insteadof two phases, we even saw quite complex divisionsof work: certain teams were able to split into subgroupsexploring alternative paths (in this case, one group ex-plored the showroom, another worked on the other stake-holders). Some of these explorations led to new knowl-edge (in this case, the value of manufactured goodsexplored from the stakeholders perspective) that wasused by the other subgroup (a showroom that helps themanufacturers to enhance the value of their products).

4.3. Conclusion and discussion on the evaluation ofPBCL and its relevance

In this paper, we began with a summary of how recent ad-vances on creativity help clarify the objectives of methodsteaching how to be creative in engineering design. Whereasclassical objectives strictly concern the outputs (creativity cri-teria), these works identify process criteria and focus teachingon the capacity to overcome the factors that limit creativityduring the process. We identified four types of FEs (in thegeneration of alternatives, knowledge acquisition, collectivecreativity, and creativity process). We then showed how theC-K theory can help to teach how to overcome the FEs: C-

Fig. 7. The second example of concept–knowledge (C-K) diagram prepared by students educated in C-K. The white letters on a dark back-ground in K indicates knowledge expansion, that is, knowledge acquired during the process (mainly through Internet connections); thewhite letters on a dark background in C indicates an expansive partition in C, that is, partitions that add unusual attributes to theconcept (e.g., the shopping cart is always provided by the supermarket, but the concept considers that part of the shopping cart is notprovided by the supermarket). [A color version of this figure can be viewed online at journals.cambridge.org/aie]

Teaching innovative design reasoning with C-K theory 89

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K theory enables students to model the FEs, to identify andcontrol them in the process, and finally, to overcome them.We illustrated this process with two types of educationalcase studies, based on a type of PBL that is strongly scaf-folded by a theory.

It is interesting to note that the method helps to address de-sign issues that are not easily address in the classical Simo-nian approach: in “wicked problems” (following Rittel &Webber, 1972) with “figural complexity” (following Schon,1990), certain design solutions are not easily connected to thedesign starting point. Our reading of the FE literature clarifiedwhat “easily” means: we identified four “obstacles,” that is,four FEs. In front of these FEs C-K is not a “mechanical” pro-cess; C-K theory provides the designer with a means to beself-reflective: C-K theory helps the practitioner to becomeaware of his own fixations and to overcome them. Hence, itshould make him more able to make connections betweenpieces of knowledge that are not easy to connect.

We emphasize the need to address two strong issues ofPBL in innovative design: evaluation and relevance.

Evaluation may be difficult if one has to evaluate “propo-sitions that are neither true nor false.” However, the evalu-ation must account for the capacity to overcome the four typesof FEs. It is not the C-proposition in itself that should be eval-uated but the distribution of the C-propositions, related to theknowledge base. This is why four types of criteria can beused:

1. for FEs in the generation of alternatives: variety, origin-ality, capacity to find the classical solutions, and posi-tion original ones;

2. for FEs in knowledge acquisition: robustness, value, ca-pacity to build a rigorous state of the art review that doesnot neglect any aspect of the concept (science, uses, in-novation competition, etc.), and also includes provoca-tive examples;

3. for FEs in collaborative creativity: capacity to divide thework, to conduct contrasted explorations that will helpeach other (through sharing the newly acquired knowl-edge); and

4. for FEs in the creativity process: capacity to maintainbroad exploration at all levels in C and also to workon “quick and fast solutions” to speed up the innovationprocess.

One of the recurrent issues in PBL is to ensure that the caseis relevant. Is the chosen project “real” enough? One way toassess this is to compare the educational project and real-life projects. For instance, we can compare the educationalconcept (smart shopping cart) with other industrial cases.The IDEO case shows that the concept was actually an accep-table concept for an industrial design firm. We can also com-pare the type of knowledge bases available in “real life” andthe type of knowledge bases available in our cases: the stu-dents are not experts in shopping carts and have no educationin this kind of product. However, this situation is quite cred-

ible because, in most companies, innovative design briefsmake the actors go far beyond company expertise. Finally,we can compare the type of organizations in both cases.This is certainly the strongest difference, because no particu-lar organization is specified in the C-K exercise, whereascompanies often have strongly structured organizational pro-cesses. However, in both cases the professionals, as well asstudents, have to more or less invent a new form of organiza-tion. In this sense, the learning situation is doubtless quite rea-listic! Note that Mines Paristech has developed another wayto address this question: one educational solution consistsin organizing C-K interventions within companies, with com-pany experts, and within their organizational setups (this edu-cational method is not described here).

However, the issue of realism should be based on the cri-terion to be met by teaching, namely, “do students overcomethe FEs?” The question becomes: does the exercise realisti-cally illustrate the FE? In this perspective, one interest ofthe C-K-based PBCL is precisely to clarify the FEs to whichstudents were submitted and then to analyze whether theseFEs are realistic or not. In the case of the shopping cart,this gives the following analysis:

† FEs in the generation of alternatives: The FE appearedwhen the display was seen as an accessory mounted onthe shopping cart (solved by providing the plug for theuser’s own display device) or the shopping cart for thesupermarket of today (solved by the showroom shop-ping cart). The students were hence “fixed” by the factthat the environment (the supermarket) was not in thescope of the work and that the firm’s product (the shop-ping cart) had to be improved upon rather than signifi-cantly changed. This seems quite realistic, in the sensethat engineers in a company would probably also be“fixed” in a similar way. Note that it helps to answerone critical question: is the initial brief “realistic”? Itcould be argued that the initial brief (smart shoppingcart) tended to provoke the FE; and also that a brieflike “a smart system to help the shopper to shop in asupermarket” would diminish the FE. It is difficult toanswer this question without clarifying what the FE inthe second case would be. However, we understandthat, in an educational perspective, the real issue is notwhether the brief “provokes” an FE but that the FE itselfis realistic.

† FE in knowledge acquisition: The FE appeared throughthe clarification of the initial knowledge base. The stu-dents apparently knew rather less than shopping cart de-signers. This means that the FE is somewhat weaker forstudents than for experts. The exercise therefore tends tounderestimate the “knowledge acquisition FE.”

† FE in collaborative creativity: It appeared when partic-ipants were reluctant to break away from the “legiti-mate” knowledge base or to propose “crazy concepts”and preferred to only build on the common knowledgebase and work on their own ideas. In the exercise, there

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was no real issue in the “legitimacy” of expertise and inthe risks taken by “rule breakers.” Hence, the exercisealso minimized the collective creativity FE.

† FE in the creativity process: It appeared when partici-pants tended to use the classical “funnel” pattern, witha first phase of idea generation and a second phase of“development.” In this case as well, the exercise under-estimates this type of FE.

This analysis helps to qualify the extent to which the smartshopping cart exercise was realistic, showing that it is morerealistic when addressing the two first FEs than the last two.

ACKNOWLEDGMENTS

This work was carried out with the financial support of the DesignTheory and Methods for Innovation Research and Teaching Chair.

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Armand Hatchuel is a Professor and Deputy Director of theCenter for Management Science at MINES ParisTech. Heproposed the C-K design theory in 1996. He has publishedseveral books and papers and is a member of scientific boardsin France and Sweden. Dr. Hatchuel received the medal of theParis School of Arts and Crafts in 2003 for his work on designtheory, and he was elected to the Academy of Technologies in2009. He co-chairs the Design Theory SIG of the DesignSociety and coordinates the Chair of Design Theory andMethods for Innovation. His work is concentrated on the the-ory of management and design.

Pascal Le Masson is a Professor of design, project, and man-agement at MINES ParisTech. He and Benoit Weil are respon-sible for the Engineering Design and Innovation Teaching Pro-gram at Mines ParisTech. Dr. Le Masson has published severalpapers. His last book, Strategic Management of Innovation andDesign, which he coauthored with Armand Hatchuel and Be-noit Weil, will be published by Cambridge University Pressin 2010. The focus of his research is on the management of in-novative design capabilities. It is motivated by issues raised bycompanies striving for growth by intensive innovation and thenew theoretical paths opened by C-K theory.

Benoit Weil is a Professor of design, project, and manage-ment at MINES ParisTech. He and Armand Hatchuel createdthe Research Program on Design Activities. He also workedwith Armand on C-K theory to account for the dual expansionof knowledge and concepts. They recently showed strikingsimilarities between C-K theory and set theory. Dr. Weil isnow leading the Research Program on Design Regimes (fun-ded by the French National Research Agency) in collabora-tion with Armand Hatchuel, Pascal Le Masson, and BlancheSegrestin. The purpose of this program is to study the mainfeatures of innovative design regimes in several sectors. Hisresearch focuses on the rationalizations of collective actions.

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