Torrance Test of Creative Testing (TTCT)
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BiblioMap 
Synonyme
Torrance Test of Creative Testing, TTCT
Definitionen
Perhaps the most widely used creativity test is the Torrance Test of Creative Thinking (TTCT). Developed in 1966 and currently used worldwide, the TTCT measures several dimensions of creative potential, including intellectual curiosity, open-mindedness, verbal expressiveness, and originality. Though not without its critics, the TTCT has been found to predict creative achievement better than other standard measures of creative or divergent thinking.7 Empirical evidence suggests that high scores on the test successfully predict subsequent creative careers and accomplishments.8The TTCT offers a suite of authentic activities that prompt the
test-taker to engage in various types of thinking that mirror the kinds of
creativity required for real-life and daily human operations, including
asking questions, guessing causes and consequences, improving a
product, and utilizing imagination (STS, 2017). In total, the TTCT includes
six distinct creative activities designed to evaluate the operation
of creativity as it is often used in business and everyday life. In addition
to a standard Alternative Uses Task (Unusual Uses), the TTCT also includes
tasks to evaluate Asking Questions, Guessing Causes, Guessing
Consequences, and Product Improvement.
Von Erik E. Guzik, Christian Byrge, Christian Gilde im Text The originality of machines (2023) 
Bemerkungen
The strength of the TTCT is its large database
of historical human responses that can be used as a control and comparison
group. This has become possible because of its consistency in
using the same demographic makeup of test-tasker for decades and
because responses have been systematically collected for scoring.
Von Erik E. Guzik, Christian Byrge, Christian Gilde im Text The originality of machines (2023) The TTCT is a commercially protected assessment instrument,
therefore prompts are not accessible to ChatGPT or any other AI system,
or publicly available in general. This offers a unique opportunity to test
ChatGPT using specific prompts that it likely has never been asked
before. ChatGPT will have to generate the responses, not simply retrieve
them from ist database.
Von Erik E. Guzik, Christian Byrge, Christian Gilde im Text The originality of machines (2023) Current product-based measures like the TTCT also raise interesting
questions about creativity assessment in general. For example, which
human raters are capable of best measuring human creativity? And
which of these raters are capable of measuring AI creativity? In addition,
is a truly accurate and unbiased assessment of creativity and originality
possible within current assessments? A study by Licuanan et al. (2007)
found that participants preferred ideas of low originality when evaluating
highly original ideas. They also found that ideas of high originality
were discounted because raters lacked the knowledge for accurately
recognizing the originality of these ideas.
Von Erik E. Guzik, Christian Byrge, Christian Gilde im Text The originality of machines (2023) While the TTCT has long been considered a valid and reliable measure
of creativity, the results of GPT-4 testing may simply highlight the
limitations of existing creativity assessments. Although GPT-4 demonstrated
high fluency, flexibility, and originality, assessments such as the
TTCT may not fully capture the nuances and complexities of human
creativity especially as related to person, process, and press—in this
respect, current human scores on tests such as the TTCT may understate
or incompletely measure human creativity. Further, the performance of
GPT-4 could suggest that traditional creativity assessments need to be
revised to differentiate between human and AI-generated creative outputs
and evaluate other aspects of creativity beyond those currently
assessed, including processes of convergent thinking.
Von Erik E. Guzik, Christian Byrge, Christian Gilde im Text The originality of machines (2023) Verwandte Objeke
Verwandte Begriffe (co-word occurance) |
Häufig co-zitierte Personen
Robert J.
Ben
MitchelStatistisches Begriffsnetz
Zitationsgraph
6 Erwähnungen
- Kreativität im Informatikunterricht (Ralf Romeike) (2008)
- Cognitive control in media multitaskers (Eyal Ophir, C. Nass, Anthony D. Wagner) (2009)
- Design Thinking Research - Studying Co-Creation in Practice (Hasso Plattner, Christoph Meinel, Larry Leifer) (2012)
- The App Generation - How Today's Youth Navigate Identity, Intimacy, and Imagination in a Digital World (Howard Gardner, Katie Davis) (2013)
- Postformal Education - A Philosophy for Complex Futures (Jennifer M. Gidley) (2016)
- The originality of machines - AI takes the Torrance Test (Erik E. Guzik, Christian Byrge, Christian Gilde) (2023)
Biblionetz-History 
