Artificial Intelligence Review

, Volume 48, Issue 4, pp 529–555 | Cite as

A computational model of design critiquing



One of promises of computer-based critiquing systems is that they will help designers improve their solutions in an intelligent manner. Historically, they have tended to concentrate on the representation of the domain, the representation of the user’s knowledge, and a wide variety of communication skills. Nevertheless, the issue of the critiquing competence of the critiquing systems is important; today’s critiquing systems have the limited range and adaptability as compared to the wealth of critiquing strategies employed by human design teachers. Therefore, this paper presents a computer-based critiquing system named the Furniture Design Critic as a computational model of design critiquing based on a systematic understanding of design critiquing practice. This system has the potential to help us implement and formulate a wide range of human critiquing strategies, specifically determining which critiquing methods are selected under which critiquing conditions.


Design critiquing system Design critiquing Critiquing strategies 


  1. Ali NM, Hosking J, Grundy J (2013) A taxonomy and mapping of computer-based critiquing tools. IEEE Trans Softw Eng 39:1494–1520CrossRefGoogle Scholar
  2. Anderson JR, Corbett AT, Koedinger KR, Pelletier R (1995) Cognitive tutors: lessons learned. J Learn Sci 4:167–207. doi: 10.1207/s15327809jls0402_2 CrossRefGoogle Scholar
  3. Anthony KH (1991) Design juries on trial: the renaissance of the design studio. Van Nostrand Reinhold, New YorkGoogle Scholar
  4. Bailey RON (2004) The digital design coach: enhancing design conversations in architecture education. Victoria University of Wellington, WellingtonGoogle Scholar
  5. Bergenti F, Poggi A (2000) Improving UML design using automatic design pattern detection. In: Proceedings of the 12th international conference on software engineering and knowledge engineering, pp 336–343Google Scholar
  6. Boyer EL, Mitgang LD (1996) Building community: a new future for architecture education and practice? A special report. Carnegie Foundation for the Advancement of Teaching, PrincetonGoogle Scholar
  7. Chun HW, Ming-Kit Lai E (1997) Intelligent critic system for architectural design. IEEE Trans Knowl Data Eng 9:625–639CrossRefGoogle Scholar
  8. de Souza CRB, Oliveira HLR, da Rocha CRP et al (2003) Using critiquing systems for inconsistency detection in software engineering models. In: Proceedings of the fifteenth international conference on software engineering and knowledge engineering (SEKE 2003). San Francisco, CA, USA, pp 196–203Google Scholar
  9. Fischer G (1987). A critic for LISP. In: Proceedings of the 10th international joint conference on artificial intelligence, Milan, Italy, pp 177–184Google Scholar
  10. Fischer G (1989) Human–computer interaction software: lessons learned, challenges ahead. IEEE Softw 6:44–52CrossRefGoogle Scholar
  11. Fischer G, Morch A (1989) JANUS: integrating hypertext with a knowledge-based design environment. In: ACM conference on hypertext and hypermedia. ACM Press, Pittsburgh, pp 105–117Google Scholar
  12. Fischer G, Nakakoji K (1997) Computational environments supporting creativity in the context of lifelong learning and design. Knowl Based Syst 10:21–28. doi: 10.1016/S0950-7051(97)00010-5 CrossRefGoogle Scholar
  13. Fischer G, McCall R, Morch A (1989) Design environments for constructive and argumentative design. ACM SIGCHI Bull 20:269–275. doi: 10.1145/67450.67501 CrossRefGoogle Scholar
  14. Fischer G, Lemke A, McCall R, Morch A (1991a) Making argumentation serve design. Hum Comput Interact 6:393–419. doi: 10.1207/s15327051hci0603&4_7 CrossRefGoogle Scholar
  15. Fischer G, Lemke AC, Mastaglio T, Morch AI (1991b) The role of critiquing in cooperative problem solving. ACM Trans Inf Syst 9:123–151CrossRefGoogle Scholar
  16. Fu MC, Hayes CC, East EW (1997) SEDAR: expert critiquing system for flat and low-slope roof design and review. J Comput Civ Eng 11:60–68CrossRefGoogle Scholar
  17. Gertner AS, Webber BL (1998) TraumaTIQ: online decision support for trauma management. IEEE Intell Syst 13:32–39CrossRefGoogle Scholar
  18. Goldschmidt G, Hochman H, Dafni I (2010) The design studio “crit”: teacher–student communication. Artif Intell Eng Des Anal Manuf 24:285–302. doi: 10.1017/S089006041000020X CrossRefGoogle Scholar
  19. Langlotz CP, Shortliffe EH (1983) Adapting a consultation system to critique user plans. Int J Man Mach Stud 19:479–496CrossRefGoogle Scholar
  20. Lynch C, Ashley K, Mitrovic T (2010) Intelligent tutoring technologies for Ill-defined problems and Ill-defined domainsGoogle Scholar
  21. Mastaglio T (1990) User modeling in computer-based critics. In: IEEE The 23rd annual Hawaii international conference on system sciences. IEEE Press, Kailua-Kona, pp 403–412Google Scholar
  22. Mitrovic A, Weerasinghe A (2009) Revisiting ill-definedness and the consequences for ITSs. In: Frontiers in artificial intelligence and applications, pp 375–382Google Scholar
  23. Mitrovic A, Mayo M, Suraweera P, Martin B (2001) Constraint-based tutors: a success story. Eng Intell Syst. doi: 10.1007/3-540-45517-5_103 MATHGoogle Scholar
  24. Mitrovic A, Koedinger K, Martin B (2003) A comparative analysis of cognitive tutoring and constraint-based modeling. In: Proceedings of the international conference on user modelling, vol 2702, pp 313–322. doi: 10.1007/3-540-44963-9_42
  25. Mitrovic A, Martin B, Suraweera P (2007) Intelligent tutors for all: the constraint-based approach. IEEE Intell Syst 22:38–45. doi: 10.1109/MIS.2007.74 CrossRefGoogle Scholar
  26. Nakakoji K, Yamamoto Y, Suzuki T et al (1998) From critiquing to representational talkback: computer support for revealing features in design. Knowl Based Syst 11:457–468CrossRefGoogle Scholar
  27. Ochsner JK (2000) Behind the mask: a psychoanalytic perspective on interaction in the design studio. A Archit Educ 53:194–206. doi: 10.1162/104648800564608 Google Scholar
  28. Oh Y, Do EY-L, Gross MD (2004) Intelligent critiquing of design sketches. In: Davis R (ed) AAAI (American Association for Artificial Intelligence) fall symposium—Making pen-based interaction intelligent and natural. The AAAI Press, Washington, DC, pp 127–133Google Scholar
  29. Oh Y, Ishizaki S, Gross MD, Do EY-L (2013) A theoretical framework of design critiquing in architecture studios. Des Stud 34:302–325CrossRefGoogle Scholar
  30. Ohlsson S (1996) Learning from performance errors. Psychol Rev 103:241–262. doi: 10.1037/0033-295X.103.2.241 CrossRefGoogle Scholar
  31. Qiu L, Riesbeck CK (2004) An incremental model for developing educational critiquing systems: experiences with the Java Critiquer. In: Proceedings of world conference on educational multimedia, hypermedia & telecommunications (ED-MEDIA). Switzerland, pp 171–175Google Scholar
  32. Robbins JE (1998) Design critiquing systems. Department of Information and Computer Science, University of California, IrvineGoogle Scholar
  33. Robbins JE, Redmiles DF (1998) Software architecture critics in the argo design environment. Knowl Based Syst 11:47–60CrossRefGoogle Scholar
  34. Schön DA (1983) The reflective practitioner: how professionals think in action. Basic Books Inc., New YorkGoogle Scholar
  35. Schön DA (1985) The design studio. RIBA, LondonGoogle Scholar
  36. Silverman BG (1992) Survey of expert critiquing systems: practical and theoretical frontiers. Commun ACM 35:106–127CrossRefGoogle Scholar
  37. Simon HA (1969) The sciences of the artificial. MIT Press. Cambridge. doi: 10.1016/S0898-1221(97)82941-0
  38. Souza CRB, Ferreira Jr JS, Goncalves KM, Wainer J (2000) A group critic system for object-oriented analysis and design. In: Proceedings of ASE 2000 (the fifteenth IEEE international conference on automated software engineering), pp 313–316Google Scholar
  39. Uluoǧlu B (2000) Design knowledge communicated in studio critiques. Des Stud 21:33–58. doi: 10.1016/S0142-694X(99)00002-2 CrossRefGoogle Scholar
  40. Weaver N, O’Reilly D, Caddick M (2000) Preparation ans support of part-time teachers: designing a tutor training programme fit for architects. In: Nicol D, Pilling S (eds) Changing architectural education: towards a new professionalism. Taylor & Francis Spon Press, New York, pp 265–273Google Scholar
  41. Woodbury R, Burrow A, Drogemuller R, Datta S (2000) Code checking by representation comparison. Computer Aided Architecture Design Research in Asia (CAADRIA), pp 235–244Google Scholar
  42. Ye Y (2003) Programming with an intelligent agent. IEEE Intell Syst 18:43–47. doi: 10.1109/MIS.2003.1200727 Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  1. 1.Research and Development DivisionHyundai Engineering & ConstructionGyeonggi-doRepublic of Korea
  2. 2.Department of Mechanical & System Design EngineeringHongik UniversitySeoulRepublic of Korea

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