Spontaneous Anthropocentric Language Use in University Students’ Explanations of Biological Concepts Varies by Topic and Predicts Misconception Agreement

INTRODUCTION

Overview

Science education is aimed at inspiring and producing scientific thinkers, providing them with scientific knowledge as well as a mental toolkit geared for complex problem-solving. However, only recently have researchers begun to understand how science learners develop this toolkit and how emerging expertise gained through formal education may interact with intuitive conceptual frameworks, or pre-existing ways of organizing concepts and reasoning about the world. Research in learning sciences has demonstrated the importance of intuitive frameworks, emphasizing that people “come to formal education with a range of prior knowledge, skills, beliefs, and concepts that significantly influence what they notice about the environment and how they interpret it” (Bransford and National Research Council, 2004, p. 10; see also Nersessian, 1985; Duit et al., 2013; Nadelson et al., 2018; Vosniadou, 2019, 2020). Research in cognitive science suggests that these intuitive frameworks are present from early in development; as children actively seek to understand, explain, and predict the world around them, they construct intuitive conceptual frameworks as coherent systems of knowledge about the psychological, social, physical, and biological worlds (Keil, 1989; Wellman and Gelman, 1992; Carey, 2000; Gopnik and Wellman, 2012). While intuitive frameworks can help us navigate the complex world around us, they may also create obstacles to science education when they clash with science concepts (Kampourakis and Zogza, 2008; Opfer et al., 2012; Shtulman, 2017; Stern et al., 2018, 2023; Vosniadou, 2019).

In this paper, we examine undergraduate life science understanding from this perspective. Specifically, we have three major aims in this study that expand our previous work on this topic. First, we seek to establish how students spontaneously use specific intuitive frameworks—cognitiveconstruals—in response to open-ended questions across distinct biological concepts—from evolution to genetics to ecosystems. Second, we examine how students’ usage of these frameworks differs across these topics. Third, we explore the relations between students’ spontaneous use of cognitive construals in their open-ended explanations and their agreement with intuitively seductive statements that contradict current scientific understanding (i.e., misconceptions).

Cognitive Construals and Biology Education

Research in cognitive science has highlighted the impact of intuitive ways of thinking—which we term cognitive construals (see Coley and Tanner, 2012)—on how people understand and reason about the natural world (Shtulman and Schulz, 2008; Kelemen, 2012; Coley and Tanner, 2015). Cognitive construals are informal patterns of thinking about the world that inform and constrain how people make sense of new information (Coley and Tanner, 2012; see also Murphy and Medin, 1985; Smith et al., 1994; Vosniadou, 2002). These frameworks first appear in childhood but often persist into adulthood, sometimes competing with scientific information (e.g., Shtulman and Valcarcel, 2012), other times being harmonized with them (e.g., Vosniadou, 1994). For example, children (and many adults) tend to intuitively believe that all individuals within a species share the same underlying essence which gives rise to observable properties (e.g., Gelman, 2003; Taylor et al., 2009). This assumption may facilitate learning that genes provide the information for building biological structures—and indeed, that underlying, nonobvious causes are critically important throughout biological science—but can also interfere with understanding genetic variation required for evolution (e.g., Shtulman and Schulz, 2008; Bruckermann et al., 2021). In our previous work, we have identified three of these construals—essentialist thinking, teleological thinking, and anthropic thinking—which seem to be particularly useful for describing persistent ways that individuals reason about biological concepts across topics (e.g., Coley and Tanner, 2012, 2015; Richard et al., 2017; Stern et al., 2018; Brown et al., 2020).

Cognitive construals, as intuitive frameworks, are typically useful in helping us to understand and navigate the natural world. However, they have also been shown to be related to misconceptions (Coley and Tanner, 2015; Stern et al., 2018; Brown et al., 2020). Misconceptions, also known as alternative or preconceptions, are inaccurate, intuitive understandings that persist even after learners have been exposed to formal, scientific information (Coley and Tanner, 2012; Vosniadou, 2019). Importantly, these are not isolated factual errors, but rather exist as parts of interconnected conceptual frameworks. There is ongoing debate about the nature of misconceptions (Smith et al., 1994; Chi, 2013; Coley and Tanner, 2015; Vosniadou, 2020). Some theorists have posited that misconceptions arise from faulty intuitive theories (e.g., Posner et al., 1982) or are faulty extensions of productive knowledge (e.g., Smith et al., 1994). Such theories have led to significant pushback from scholars of biology education who see “misconception” as a pejorative and inadequate construct (Crowther and Price, 2014; though see Maskiewicz and Lineback, 2013; Leonard et al., 2014). Other researchers frame misconceptions not as faulty missteps, but as part of the process of learning, in which new scientific knowledge is integrated into existing intuitive frameworks and used to make sense of the world (Evans and Rosengren, 2018; Vosniadou, 2019, 2020). Our research aligns with this perspective, validating the potential usefulness of cognitive construals while also examining associated misconceptions to understand how intuitive thinking plays a role in biology learning.

Essentialist Thinking.

Essentialist thinking is based on the belief that an organism contains an underlying, defining feature or property that leads to observable characteristics (Medin and Ortony, 1989; Gelman, 2003). In some instances, this internal feature or “essence” is thought to be causal—determining the category to which an organism belongs (Gelman, 2003) and giving rise to observable properties. This type of reasoning enables rapid categorization and efficient predictions to be made about members of specific species or categories based on relatively little information (Ahn and Kim, 2005; Xu and Coley, 2022). Essentialist reasoning tends to fall into two main dimensions: naturalness beliefs and cohesiveness beliefs (Haslam et al., 2000; Coley et al., 2017). Naturalness beliefs refer to the idea that category membership is naturally-occurring and cannot be changed, as well as the idea that there are strict, insurmountable boundaries between categories (i.e., the categories are discrete; Gelman and Wellman, 1991). Cohesiveness beliefs center on emphasizing similarities between group members, minimizing in-group variability, and implying that shared characteristics will appear over time due to the shared causal essence (e.g., assuming group homogeneity; Gelman and Wellman, 1991). Essentialist thinking is likely an early-emerging cognitive default; it is evident in very young children (Ahn et al., 2001; Gelman, 2003; Graham et al., 2016), and is observed across cultures (Gelman, 2003; Tsukamoto et al., 2013; Smyth et al., 2017; Davoodi et al., 2020; Xu and Coley, 2022). Essentialist thinking can also become more pronounced in adults under cognitive load, suggesting that it is inhibited rather than replaced by scientific knowledge, and re-emerges when inhibitory control is reduced (Eidson and Coley, 2014; Siddiqui and Rutherford, 2021).

Moreover, essentialist thinking is commonly used by undergraduate students to understand and explain biological concepts (Shtulman and Schulz, 2008; Coley and Tanner, 2012, 2015; Coley et al., 2017; Richard et al., 2017; Bruckermann et al., 2021). This may, in part, be attributed to the use of essentialist language in biology classrooms, as instructors responsible for teaching biological concepts continue to rely on essentialist language to help convey these complex ideas (Betz et al., 2019). In such settings, essentialist thinking might remain helpful for reasoning about categories or species, with an emphasis on their shared traits, innate potentials, and underlying similarity despite surface variation.

However, essentialist reasoning also can lead to misconceptions about biology. Previous work has primarily focused on how essentialist reasoning can negatively impact learning about evolution (Kampourakis and Zogza, 2008; Shtulman and Shultz, 2008; Opfer et al., 2012; Stern et al., 2018; Stern et al., 2023). Reasoning that an entire group reflects cohesiveness (shares many features and causal essences) leads students to assume that there is little within-species variability, and that an adaptation occurs uniformly and rapidly across an entire species. This idea conflicts with the core evolutionary concept of natural selection: that random mutations in a small number of organisms is beneficial over many subsequent generations until the mutation is widely shared across said species (Kampourakis and Zogza, 2008; Shtulman and Schulz, 2008; Gelman and Rhodes, 2012). Likewise, beliefs about naturalness can lead students to see species as discrete, static units, intensifying boundaries between them, rather than descendants of common ancestors (Coley and Muratore, 2012). Likewise, essentialist thinking could give rise to assumptions of simple 1:1 mapping between genotype and phenotype, or beliefs that ecosystems are static and unchanging, rather than dynamic. Given these potential barriers to understanding, unraveling the effects of essentialist thinking is vital for science education research.

Teleological Thinking.

Teleological thinking is the tendency to view entities and phenomena in terms of their purpose or function. It is a form of causal reasoning that involves asserting that a function, purpose or outcome causes a given object or entity's existence. One example of teleology would be the belief that humans wear coats to protect themselves from the cold. In this example, the phenomenon of wearing a coat is attributed to its purpose—protecting us from the cold. Another example of teleology would be the idea that rain falls so that it can water plants. In this example, the existence of rain is explained by its function—watering plants. In the first example, the use of teleology led to a correct conclusion—many people do in fact wear coats in order to protect themselves from the cold. In the second example, however, teleological thinking leads to an incorrect conclusion–rain just so happens to water plants, that is not its purpose. When people overextend teleological thinking to explain natural phenomena in such a way, it is known as “promiscuous teleology” (e.g., Kelemen, 1999; 2012).

Because people generalize teleology to natural kinds, it can impact biology learning. Some researchers and philosophers of biology argue that such reasoning is not necessarily out-of-step with scientific understanding (see Brandon, 1981; Lennox, 1993; Lennox and Kampourakis, 2013). Indeed, positing relations between cause and effect is core to appropriate biological thinking (Evans and Rosengren, 2018; Trommler and Hammann, 2020), and may help learners grasp important connections between structure and function. However, teleological reasoning can also lead to misconceptions about biology and the natural world. This has primarily been investigated with respect to understanding evolution and genetics. In evolution, teleological thinking can give rise to “design stance” (Kampourakis, 2020), leading students to view evolution as directional and purpose-driven rather than the group-level proliferation of random, but advantageous, traits (Gregory, 2009; Stern et al., 2018; Gresch and Martens, 2019; Brown et al., 2020; González Galli et al., 2020; Gresch, 2020; Trommler and Hammann, 2020). Such misconceptions about evolution can have significant downstream consequences with real-world implications, including negative impacts for understanding genetics (Stern et al., 2020; Stern et al., 2023), and medical and agricultural issues like antibacterial and pesticide resistance (Gregory, 2009; Richard et al., 2017; Pickett et al., 2022). Teleological thinking can also lead to misconceptions about ecosystem interactions (e.g., beliefs that plants produce oxygen so that animals can breathe, or that fungi grow in forests in order to help decomposition). Teleological misconceptions persist past elementary and secondary education, appearing widespread in undergraduate biology students (e.g., Coley and Tanner, 2015; Coley et al., 2017; Stern et al., 2018). For example, Kelemen and Rosset (2009) found that undergraduate students endorsed such statements as “fungi grow in forests to help decomposition” and “finches diversified in order to survive,” and endorsed them particularly strongly when tested under speeded conditions. Thus, teleological thinking appears to be persistent across development and give rise to systematic misconceptions across multiple levels of education.

Anthropic Thinking.

A third construal is anthropic thinking, a broad umbrella term for reasoning that frames understanding of the natural world in human terms. Anthropic thinking can be further subdivided into two types: anthropomorphism—when an individual attributes human-like qualities such as motivation, emotion, and cognition to nonhuman entities (Inagaki and Hatano, 1991; Epley et al., 2007)—and anthropocentrism—when an individual centers their understanding of the natural world on humans (Coley et al., 2017; Arenson and Coley, 2018; Betz and Coley, 2022). Anthropic thinking, unlike essentialist or teleological thinking, does not appear to be a developmentally persistent cognitive default. It does not appear early in development (Waxman et al., 2007; Herrmann et al., 2010; Wilks et al., 2021) and varies markedly across culture and experience (Ross et al., 2003; Bang et al., 2007; Herrmann et al., 2010; Xu and Coley, 2022). Moreover, unlike teleological and essentialist thinking, anthropic thinking is reduced rather than facilitated, under time pressure (Arenson and Coley, 2018; Betz and Coley, 2022). These findings suggest that anthropic thinking may be a learned cognitive strategy rather than an early emerging cognitive default (Arenson and Coley, 2018).

Anthropic reasoning is commonly used in biology education, from preschool (Kallery and Psillos, 2004; Thulin and Pramling, 2009) to secondary school (Quinn et al., 2016) and university biology classrooms (Betz et al., 2019). For instance, undergraduate biology instructors used anthropic language significantly more than teleological or essentialist language, a pattern that held for lower- and upper-division biology courses for majors as well as courses for nonmajors, across two universities with very different student demographics (Betz et al., 2019). Instructors might use anthropic language to help them explain novel biological concepts using a familiar organism—Homo sapiens—to facilitate student understanding (as suggested by teachers in Kallery and Psillos, 2004). Indeed, some research shows that both anthropomorphism (Byrne et al., 2009; Betz et al., 2019; Kamil et al., 2020) and anthropocentrism (Nielson, 2023; Thor et al., in prep) can be related to positive learning outcomes. For example, greater anthropocentrism is related to increased agreement that humans are the main drivers of climate change (Betz and Coley, 2022). In addition, using humans as an exemplar species has been shown to increase student interest and memory for biology learning in domains such as evolution (Nettle, 2010; Pobiner et al., 2018) and health literacy (Jacque et al., 2016).

However, anthropic thinking, particularly anthropocentric thinking, has also shown to be related to a variety of biological misconceptions. For example, anthropocentric reasoning has been shown to be related to biological misconceptions about bacteria (e.g., Byrne et al., 2009) and antibiotic resistance (e.g., Richard et al., 2017; Nielson, 2023). In addition, anthropocentrism can lead to over-attribution of human characteristics to similar organisms, or underattribution of universal biological characteristics (such as life itself) to dissimilar organisms (Inagaki and Hatano, 1991; Inagaki and Sugiyama, 1994; Carey, 2000). This kind of anthropocentrism has also been associated with negative attitudes toward environmental protection (Kopnina et al., 2018; Betz and Coley, 2022) and animal welfare (Caviola et al., 2019; Fortuna et al., 2023).

Summary.

To be clear, cognitive construals do not always lead to scientifically incorrect conclusions. Essentialist thinking may lead to a search for underlying cause, which is critical for understanding concepts such as genetics, metabolism, and neuroscience. Teleological thinking may lead to an understanding of the correspondence of form and function. As such, intuitive cognitive construals may serve as bridges to more mature scientific understanding. However, as illustrated above, such thinking can and does lead to misconceptions when the state of the art in biological science conflicts with intuitive ways of understanding.

Outstanding Questions

Despite the range of research on cognitive construals in biology education, there remain outstanding questions about the role of construal-consistent thinking in undergraduate biology learning. First, more research is needed to determine whether students use construal-consistent language (CCL) in a spontaneous way, without immediate influence from instructors or research materials, and whether they do so across biology concepts. Studies that examine intuitive thinking across multiple biology topics have typically examined CCL use in response to “challenge statements” that contain CCL, rather than what students might spontaneously produce in response to an open-ended question that does not contain CCL (e.g., Coley and Tanner, 2015; Stern et al., 2018). This raises the legitimate question of whether participants were primed to use CCL by the context of responding to a statement that already contains CCL (see Gouvea and Simon, 2018; Slominski et al., 2023).

Studies that have used such “open prompts” devoid of CCL focus on a single biological topic, such as antibiotic resistance (Richard et al. 2017) or evolution (Opfer et al., 2012; Zhao and Schuchardt, 2019), rather than multiple topics. These studies have found that students still use construal-consistent thinking in response to an open prompt (Opfer et al., 2012; Federer et al., 2016; Richard et al. 2017; Zhao and Schuchardt, 2019; Hartelt and Martens, 2024), but it is not clear whether this would generalize to other topics (e.g., about genetics or plant biology). It is also likely that the type of construal-consistent thinking and language that students use varies by biology topic, especially given the links in previous literature between certain construals (e.g., teleology, essentialism) and topics (e.g., evolution, Kelemen, 1999; 2012; genetics, Dar-Nimrod and Heine, 2011; Donovan, 2022). As such, more research is needed to understand whether and how students’ use of CCL varies across a range of life science concepts, specifically using open prompt questions devoid of CCL.

Finally, more research is needed to determine the relationship between CCL and misconception agreement. A major motivator to examine cognitive construals in biology education is the contention that intuitive understanding can interfere with learning biological science by producing or promoting misconceptions (Shtulman and Shultz, 2008; Gelman and Rhodes, 2012; Coley and Tanner, 2012; 2015). Our own previous work has shown that participants who use more CCL in their explanations are also more likely to agree with construal-consistent misconceptions (Coley and Tanner, 2015; Richard et al., 2017; Nielson, 2023). However, such relationships might be driven or exaggerated by the methodology of asking students to rate their agreement with a misconception (e.g., challenge statement) and then explain their rationale for their rating. This approach could potentially increase the likelihood of correlation between the measures. As such, a stronger test of this relationship could involve examining the relationship between spontaneous use of CCL and an independent measure of misconception agreement.

The Present Study

Overall, previous research indicates that construal-consistent thinking plays a significant role in biology education, including for undergraduate students. Examining how cognitive construals impact undergraduate biology education is crucial to aid student learning, particularly in identifying and correcting misconceptions. However, much of this research has examined construal-consistent thinking in a single domain of biology (e.g., evolution, Richard et al., 2017; Brown et al., 2020), used a method that potentially primed students to use CCL (e.g., Coley and Tanner, 2015), and/or lacked independent confirmation of the association between misconceptions and CCL. In the present study, we addressed each of these outstanding questions. To assess the tendency for students to spontaneously use construal-consistent reasoning, undergraduates responded to open-ended questions absent of CCL (i.e., open prompts) asking them to explain biological concepts. Students were additionally asked the extent which they agreed or disagreed with construal-consistent challenge statements and asked to explain their reasoning. To address variability across topics, we systematically examined the relationship between students’ spontaneous use of CCL and their endorsement of misconceptions across all five Vision and Change Core Concepts (AAAS, 2011). And to assess relations between the use of CCL and agreement with misconceptions, we assessed misconception agreement independently using items separate from those used to assess use of CCL.

Research Questions and Hypotheses

Research Question 1: Do Students Spontaneously Use CCL to Explain Biological Phenomena Across Topics?

In line with the literature (e.g., Richard et al., 2017; Pickett et al., 2022), we expected that the majority of students would spontaneously generate CCL in response to an open prompt, and that this would be true across biology topics.

Research Question 2: How does Spontaneous CCL Vary by Biology Topic?

Though we had no formal hypotheses about the variation of language use between the specific topics we asked students to explain, we expected that certain construals may be more or less common depending on the topic (see Coley and Tanner, 2015). For example, one might expect students to utilize teleology at a higher rate to explain how living things develop traits well suited to their environment (given previous literature showing the relationship between teleology and evolution; Kelemen, 2012) than to explain the differences between the cells in leaves compared with flowers.

Research Question 3: What is the Relationship Between Spontaneous CCL Use and Misconception Agreement?

We hypothesized that spontaneous CCL use in response to an open prompt would positively predict participants’ agreement with construal-consistent misconception statements in a questionnaire completed later in the study, following our previous research (e.g., Coley and Tanner, 2012; 2015; Richard et al., 2017; Pickett et al., 2022). Additionally, we expected that essentialist, teleological, and anthropic language use in the open prompt would selectively predict agreement with corresponding misconception statements.

MATERIALS AND METHODS

Participants

A total of 1075 undergraduate students (694 female, 365 male, 16 nonbinary/other) were recruited from two universities in the United States. University 1 is a large urban public university on the West Coast that has ∼27,000 undergraduate students. University 2, a large urban private university on the East Coast, has ∼18,000 undergraduate students. Based on university-wide student demographics, University 1 has a more diverse student body (larger proportions of Hispanic, Asian, and Black students), whereas University 2 has a higher proportion of White and international students and higher Scholastic Aptitude Test (SAT) scores and grade point average of incoming students (College Factual, 2019). In other words, Universities 1 and 2 present strikingly different demographic profiles, and together constitute a broad sample of undergraduate students (see Appendix A for details on university demographics and race/ethnicity demographics for this sample). To ensure a wide range of biological interest, knowledge, and formal coursework, at each university undergraduates were recruited from three distinct populations: nonbiology majors (i.e., students majoring in anything not life- or health-science related), entering biology majors (i.e., first-year students majoring in biology, behavioral neuroscience, biochemistry, or health-related sciences), and advanced biology majors. Table 1 provides the number of students in each category as well as the number of biology classes they have taken. For the purposes of this paper, we focus on students as a single group, rather than on differences between groups based on class level and major, although in select analyses we use biological expertise and university as control variables to rule out alternative explanations for findings. One hundred and forty-seven participants were excluded following the removal of one module (see details under Materials and Methods). A further 10 participants were excluded due to opting out following data collection and 111 for having a major that could not be easily categorized as either a “biology major” or “nonmajor” leaving a final N of 807. We also tested biology faculty members from both universities (n = 72) as an expert comparison group; in this paper we used their data only as an external validity metric for the misconception measure.

Materials

Construal-consistent thinking about biology was assessed in three ways: we coded responses to open-ended writing prompts for the use of CCL, quantified agreement to misconception challenge statements thematically related to the open-ended writing prompt, and measured agreement with a separate, diverse set of construal-consistent biological misconceptions.

Open-ended Writing Prompts.

We developed five different modules, corresponding to the five core concepts in the Vision and Change in Undergraduate Biology Education guidelines (AAAS, 2011): Structure and Function (instantiated via items about cells), Information Flow, Exchange, and Storage (instantiated via items about DNA), Systems (instantiated via items about ecosystems), Pathways and Transformations of Energy and Matter (instantiated via items about plant growth), and Evolution. We also collected data on a sixth module on homeostasis, but based on faculty feedback we deemed the questions faulty and do not include that module in the analyses below. Each module consisted of an open prompt and three challenge statements. The open prompt was a single open-ended question which participants answered as if explaining it to a peer (e.g., “If asked by another student, how would you respond to the following question: How is it that some living things have traits that are well suited for their environment?”) in order to elicit the type of explanations and language students use in their everyday lives (see Danielson and Tanner, 2015; Potter et al., 2017; Richard et al., 2017; Pickett et al., 2022 for additional examples using this phrasing). See Table 2 for open-ended prompt wording.

Each open prompt was followed by three challenge statement prompts presenting essentialist, teleological, and anthropic misconceptions relevant to the concept of the module. For each challenge prompt (e.g., “Species develop new traits in response to their environment in order to survive.”), participants indicated their level of agreement with the statement on a Likert scale (1=Strongly Disagree to 6=Strongly Agree, plus an option for “Don't Know”), and then explained their reasoning. In this paper, we focus on the open-ended prompts rather than the challenges statements. See Appendix B, Table B1 for all statements.

Construal Consistent Misconception Measure.

This measure consisted of 25 challenge statements designed to assess agreement with construal-consistent misconceptions across the same core biological concepts addressed in modules described above. The measure included one misconception statement corresponding to each construal (essentialist, teleological, and anthropic thinking) for each core concept, for a total of 15 misconception statements. Misconceptions were drawn from published studies (e.g., Kelemen and Rosset, 2009; Shtulman and Valcarcel, 2012; Coley and Tanner, 2015) or from validated online compilations of student misconceptions (e.g., AAAS Project 2061; Project MOSART). Also included in the measure were five positive control statements (correct statements about biology, one per core concept), and five negative control statements (factually erroneous statements unrelated to our focal cognitive construals, one per core concept). These were included to provide statements that were clearly correct or incorrect so that participants felt comfortable using the entire response scale for the critical misconception items. For the purposes of this paper, we excluded these control statements from the final construal consistent misconceptions (CCM) score (see Data Scoring below). Participants rated their agreement with each statement on a Likert scale (1=Strongly Disagree to 6=Strongly Agree, plus an option for “Don't Know”). See Table 3 for examples and Appendix B2 for all items.

Design

Each student participant completed one of five modules, which included an open-ended writing prompt and three module-specific challenge prompts. Faculty participants completed two modules. The five modules were randomly distributed among participants in each testing session, such that each participant completed only one module. All participants completed the same CCM.

Procedure

Most participants were recruited through their biology courses and completed the measures in the usual classroom and time slot for their class meeting. Participants who were not recruited through their biology classrooms (i.e., nonbiology majors at University 1, nonbiology majors and some biology majors at University 2) were invited to sign up for time slots and were tested in groups in a classroom-like setting. Testing took place within a single session, where participants were first greeted by the experimenter and provided with an overview of the session. The experimenter then randomly distributed assessment packets such that each participant received one of the five modules. Participants began with the open prompt, and then completed the three challenge prompts, and were given exactly 5 min to complete each of the four prompts. Participants were instructed to wait after finishing each question so that each participant began each question at the same time. Following the completion of the module, participants were instructed to begin the CCM measure, with no time restrictions. Once all participants finished the CCM, they were instructed to complete the demographics section. Finally, participants were provided with an opt-out form, with the option of requesting that their data not be used in analysis. Once every section was complete, the experimenter collected all forms and the participants were dismissed. Faculty participants were assessed using the same materials during one-on-one meetings.

Data Scoring

Quantifying Written Responses to Open Prompts.

To assess CCL usage in open prompt responses, trained researchers coded participant explanations for essentialist, teleological, and anthropic language. Essentialist language included coding for language implying an essence-like underlying cause, as well naturalness (boundary intensification) and cohesiveness (homogeneity). Anthropic language was coded as anthropomorphic or anthropocentric. Teleological language was not divided into subtypes. The presence or absence of each type of CCL was coded in a binary manner (present/absent) for each response. Codes were not mutually exclusive, and the unit of coding was a single participant's entire response. If a type of CCL occurred at least once in a response, then it was coded as present in the response. If more than one subtype occurred in a response, the appropriate type of CCL was coded as present. For example, if homogeneity, boundary intensification, and anthropocentric language were identified in a student's explanation, that response would be coded as essentialist and anthropic CCL present, teleological CCL absent.

Every explanation was assessed by two trained coders, and disagreements were resolved by discussion to consensus. Coders were unaware of the university or groups from which responses were collected. Interrater reliability for each code met acceptable levels (Cronbach's alpha > 0.6) Coding definitions, examples, and reliability are presented in Table 4. Unless otherwise specified below, we analyze the presence/absence for each of the types (not subtypes) of CCL.

Quantifying Agreement with CCM Measure.

All participants saw the same 25 CCM items (see Table 4) and again indicated agreement from 1 (strongly disagree) to 6 (strongly agree). To focus on construal-consistent misconceptions in this paper, we omitted responses to the positive and negative control items. We scored this measure in two ways. First, we averaged scores across the remaining 15 items. This yielded a mean score ranging from 1 to 6, with higher numbers representing a stronger tendency to accept misconceptions. Scale reliability was good (Cronbach's alpha = 0.79). “Don't know” responses were rare; when they occurred, we omitted the item from mean calculations. Second, we summed the number of statements out of 15 with which students indicated that they “agreed” (i.e., to which they gave a rating of 4–6).

CCM External Validity.

Before conducting our analyses, we assessed the validity of our CCM measure, that is, that it actually quantified construal-consistent misconceptions that are inconsistent with current scientific consensus. We did this by assessing the relationship between students’ CCM scores and two separate measures, that is by assessing external validity. First, we compared student scores on this scale to faculty scores, since faculty members should be less likely to agree with statements that are based on misconceptions due to their expertise. We found that this was the case, such that students scored significantly higher on average (M = 3.09) than faculty members (M = 1.78; t (90.157) = 21.078, p < 0.001) and that students agreed with more statements on average (M = 7.49) than faculty (M = 1.94; t (97.483) = 21.516, p < 0.001). Second, we correlated CCM scores with the number of biology classes students self-reported to have previously taken, since students who had taken more classes should have a better understanding of biology and be less likely to agree with misconceptions. We found that there was also a significant negative correlation between the number of biology classes previously taken and CCM score, such that students who had taken more classes tended to agree less with misconceptions (Spearman's rho = −0.41, p < 0.001). Thus, students were more likely to agree with CCM misconceptions than faculty, and agreement decreased with increasing experience in biology classes.

RESULTS

Research Question 1: Do Students Spontaneously Use CCL to Explain Biological Phenomena across Topics?

To address Research Question 1, we examined the overall frequency of any CCL in students’ responses to open prompts across biology topics, as well as the relative frequency of each type of CCL. We found that undergraduates did use CCL to explain biological concepts across module domains. Overall, 63% of students used some kind of CCL in their open prompt response; 29% used teleological language, 26% used some type of essentialist language, and 30% used some type of anthropic language (see Figure 1). Overall frequency of the different types of CCL did not differ (McNemar's χ²(1) < 2.41, p > 0.05). Within each biology topic (i.e., Vision and Change core concept module), the majority of students (>50%) used some type of CCL, with the exception of the Systems module (44%). This suggests that CCL use is widespread when students reason about different biology topics. We explore differences between topics further in Research Question 2.

Because our coding categories were not mutually exclusive, we also examined whether different types of CCL co-occurred in students’ responses. We found that 20% of student responses contained multiple types of CCL. An example of such responses is shown in the second anthropic statement in Table 5, “Nature made sure that they had traits well suited for this environment,” where the phrase “made sure” could be considered both anthropomorphic and teleological. To statistically examine whether students who used one type of CCL were more likely to use another type of CCL, we calculated a Cochran's Q test across the three types of CCL. We found that there was no significant relationship between using one type of CCL (e.g., teleology) and using another type (e.g., essentialism) in one's response (Q = 2.75, df = 2, p < 0.05). This suggests that students’ use of each type of CCL was independent.

Research Question 2: How does Spontaneous CCL Vary by Biology Topic?

We addressed Research Question 2 in two ways. First, we systematically compared the frequency of any CCL, as well as the frequency of essentialist, teleological, and anthropic language, across the five modules, each representing a distinct Vision and Change core concept. Next, we compared the frequency of the three types of CCL within each module.

Differences in CCL Across Modules.

To evaluate whether CCL use varied by module, we conducted 2 (language presence/absence) x 5 (module) Pearson's chi-square tests of independence for any CCL, and also for each type of CCL (i.e., essentialist, teleological, anthropic). We followed up significant results by calculating standardized residuals by comparing observed frequency of language presence to a hypothesized uniform distribution (total student responses with CCL/5 modules; following Haberman, 1973; Agresti, 2012) and then noted where standardized residuals were greater than z = 1.96 (i.e., outside the 95% confidence interval).

Any CCL.

We found that students’ tendency to use any CCL varied by module (χ² (4) = 38.99, p<0.001, see Figure 2). Most modules showed presence of CCL at expected levels based on a uniform distribution across modules; only the ecosystems module showed significantly less CCL than expected (standard residual = −2.26). See Table 5 for examples of student CCL for each module.

Essentialist CCL.

Similarly, essentialist language use varied across modules (χ² (4) = 85.63, p < 0.001). Students were more likely to use essentialist language when reasoning about Structure and Function, and Energy and Matter (residuals = 2.95 and 3.98, respectively), while the Systems module had fewer essentialist responses than expected (residual = −5.38; Figure 3, center bars in blue). In the Structure and Function module on plant cell types, students frequently focused on homogeneity among plant cells by highlighting similarities instead of differences between the cell types, for example, “The cells in a leaf and cells in the flower of a plant are completely similar because they are both eukaryotic.” In the Energy and Matter module, participants tended to tie traits back to DNA as an underlying cause (e.g., “DNA is the directions that make a living thing exactly the species that it is with all of its traits”). Meanwhile, reasoning about systems may have pushed students to be less essentialist in their thinking, given the dynamicity and complexity of systems.

Teleological CCL.

Students’ use of teleological language varied by module (χ² (4) = 84.91, p > 0.001). Students were more likely to use teleological language when reasoning about Evolution, Structure and Function, and Energy and Matter (residuals = 2.63, 2.49, and 3.05, respectively) and less likely to use it when explaining Information Flow and Systems (residuals = −3.66 and −4.50, respectively), compared with a uniform distribution across all modules (see Figure 3, leftmost bar in each module, in green). That is, students explained biological processes in goal-directed ways most often when discussing the core concepts of Evolution, Structure and Function, and Energy and Matter (see Appendix B1 for all open prompt questions). For example, students’ teleological language in the Structure and Function module tended to focus on the difference in goal-directed purpose between leaf and flower cells, for example, “the leaves are there to engage in Photosynthesis and Produce energy for the Plant while the cells in the flower are there for the Purpose of reproduction for the Plant.” In the Evolution and Energy and Matter modules, students’ teleological language tended to focus on goal-directed behavior (e.g., from the Energy and Matter module on plant growth, “The plant absorbs sunlight and water in order to maintain a well balance [sic] growth.”).

Anthropic CCL.

Finally, anthropic language use also differed significantly across modules (χ² (4) = 48.36, p < 0.001). Students were more likely to use anthropic language when reasoning about Systems (ecosystems; residual = 2.70) and Information Flow (DNA; residual = 3.54). For the Systems module, participants frequently referenced humans as a major factor or destroyer in ecosystems (e.g., “Humans are the main influence in the change in the biological ecosystems over time.”). For Information Flow, students often used human genetics to explain DNA, for example, “the chromosomes, for different traits and also for diseases…gives a person a certain disease like Huntingon's [sic], color blindness etc.” In contrast, modules about Evolution (residual = −3.01) and Structure and Function (residual = −3.29) showed less anthropic language use than expected given a uniform distribution (see Figure 3, rightmost bars in gold).

Differences in CCL Use Within Modules.

Next, we compared the relative frequency of the three types of CCL within each module using Cochran's Q tests for repeated measures on a binary outcome (since each participant's response was coded present/absent for each type of CCL). We used follow-up pairwise McNemar's chi-square tests to compare frequencies of each language type within each module, with Bonferroni corrections. See Table 5 for examples of student language.

Structure and Function (Cell Types).

We found that types of CCL varied in their frequency for explanations in the Structure and Function module, which asked students to explain the difference between leaf cells and flower cells (Cochran's Q = 25.73, p < 0.001). Teleological and essentialist language were the most commonly used by participants in this module (used by 41.1% and 41.8% of students, respectively), with no significant differences between these two types of CCL. However, significantly fewer participants used anthropic language in this module than either teleological or essentialist language (17.1%; p's < 0.001).

Information Flow (DNA).

Types of CCL also varied in their frequency for explanations in the Information Flow module, which asked students to explain how living things get their traits from DNA (Cochran's Q = 38.63, p < 0.001). In contrast to Structure and Function, when explaining how living things get their traits from DNA, more students used anthropic language (46.2%) than either essentialist language (29.7%; p < 0.05) or teleological language (13.9%; p < 0.001). Essentialist language was also used by significantly more students than teleological language (p < 0.01).

Systems (Ecosystems).

Again, types of CCL also varied in their frequency for explanations in the Systems module, which asked students what influences ecosystems change (Cochran's Q = 79.52, p < 0.001). As in the InfoFlow module, anthropic language was more commonly used (38.9%) to describe influences on ecosystem change over time than teleological or essentialist language when describing (ps < 0.001). There was no significant difference between the number of participants who used essentialist and teleological language, which were both relatively infrequent (5.6% and 10.6%, respectively).

Energy and Matter (Plant Growth).

For the Energy and Matter module on how plants get materials for growth, the overall test was significant (Cochran's Q = 6.53, p < 0.05), but adjusted pairwise tests revealed no significant differences between frequency of different types of CCL. However, similar to the Structure and Function module, there were slightly more students who used teleological (43.0%) and essentialist (43.7%) compared with those who used anthropic language (31.6%), which may be due to the similar focus of this module on the biological processes of plants.

Evolution.

Finally, types of CCL also varied in their frequency for explanations in the Evolution module, which asked students to explain how some living things have traits suited for their environment (Cochran's Q = 32.15, p < 0.001). More students used teleological language (41.8%) than essentialist (19.4%) or anthropic language (18.2%) when explaining how living things have traits that are well suited for their environments (ps < 0.001). There was no difference in the number of students who used essentialist or anthropic language.

In sum, responses to each module showed a unique profile of CCL use. For two modules, a single construal was dominant (anthropic language for Systems and teleological language for Evolution). For Structure and Function, two construals—teleological and essentialist language—were most common. Information Flow showed a stair-step pattern, where anthropic language was more common than essentialist language, which in turn was more common than teleological language. And finally, for Energy and Matter, all three construals were equally frequent.

Research Question 3: What is the Relationship Between Spontaneous CCL Use and Misconception Agreement?

To address this question, we compared responses on the independent measure of misconception agreement (CCM) for students who spontaneously used CCL in their open-prompt explanations versus those who did not use CCL. We examined both participants’ mean agreement across all misconceptions (i.e., including those containing essentialist, teleological, and anthropic language) and the mean number of statements with which they agreed (i.e., selected a response of 4 - slightly agree, 5 - agree, or 6 - strongly agree).

Relations Between Spontaneous Production of CCL and Scores on CCM Measure.

If spontaneous production of CCL is associated with misconception agreement in general, then agreement scores should be higher for students who use CCL in their explanations of biological phenomena than for those who do not. To address this possibility, we first conducted Wilcoxon rank-sum tests to compare mean scores on the CCM for participants with presence vs. absence of CCL. We found that students who used any kind of CCL in their open prompt responses showed higher average agreement with misconceptions than students who did not (W = 68,203, p < 0.05), although this effect was relatively small (r = 0.088). These results suggest that spontaneous CCL use is associated with construal-consistent misconception agreement.

Decomposing the relationship by type of CCL, we found that this effect was driven by anthropic language; students who spontaneously used anthropic language in their open prompt response showed higher average agreement with misconceptions than those who did not (W = 56,792, p < 0.001; r = 0.14; see Figure 4A). In contrast, students who spontaneously used essentialist language (W = 65788.5, p > 0.05) or teleological language (W = 71184.5, p > 0.05) in their open prompt explanations showed no significant difference in mean misconception agreement compared with students who did not use these types of language. The same pattern was observed when we summed number of CCM statements that students agreed with; participants who used any kind of CCL in the open prompt agreed with significantly more misconception statements (W = 69,202, p < 0.05, r = 0.08), and this result held for anthropic (W = 58414.5, p < 0.001, r = 0.13) but not for teleological (W = 69,895, p > 0.05) or essentialist (W = 65952.5, p > 0.05) CCL use (see Figure 4B). To confirm these results, we also explored whether spontaneous production of CCL was associated with agreement with module-specific challenge statements. We found the same pattern of results when we examined agreement with module-specific misconceptions that students responded to after the open prompt; students who spontaneously used anthropic CCL in their response to the open prompt agreed with more module-specific misconceptions than those who did not, whereas no differences emerged for use of essentialist or teleological CCL (see Appendix C for details on analyses). Taken together, these results suggest that anthropic language is uniquely related to endorsing misconceptions across construals, since the CCM included teleological, essentialist, and anthropic misconceptions.

Taking a Closer Look at Anthropic CCL: Anthropocentrism or Anthropomorphism?

We found that use of anthropic CCL, but not teleological or essentialist CCL, in biological explanations was associated with higher acceptance of biological misconceptions. This raises the question of whether anthropocentric, anthropomorphic, or both kinds of CCL are implicated in this finding.

To answer this question, we conducted separate analyses on anthropocentric and anthropomorphic CCL. Students who spontaneously produced anthropocentric CCL in their open prompt responses showed significantly higher average misconception agreement on the CCM than those who did not (W = 44,624, p < 0.001; effect size: r = 0.178) whereas there was no difference between students who did and did not spontaneously produce anthropomorphic language (W = 32,815, p > 0.05; see Figure 5). The same pattern held when we summed number of misconception statements students agreed with: students producing anthropocentric CCL agreed with more misconception statements than those who did not (W = 46504.5, p < 0.001, r = 0.16), whereas no such difference emerged for anthropomorphic CCL (W = 32,260, p > 0.05).

Finally, to rule out the possibility that these associations between anthropocentric CCL and misconception agreement were an artifact of expertise or university (e.g., perhaps nonmajors, or students at just one university, were both more prone to produce anthropocentric CCL and also more prone to agree with misconceptions), we performed linear regression analysis controlling for these variables. We found that the relationships between anthropocentric language use and misconception agreement remained significant when controlling for student expertise level and university (for details, see Appendix D, Table D1). In sum, the differences in misconception agreement between students who did and did not use anthropic CCL in the open prompt were driven by the use of anthropocentric, but not anthropomorphic, CCL.

DISCUSSION

In this study, we examined the frequency of students’ spontaneous use of essentialist, teleological, and anthropic CCL and its relationship to misconception agreement across the five key concepts from the Vision and Change guidelines (AAAS, 2011). A diverse sample of undergraduate students, from nonmajors to advanced biology majors across two universities, responded to open-ended questions asking them to explain biological concepts. Students then reported their agreement with closed-ended misconception statements which were not directly related to the phenomena they explained. Our research questions focused on 1) Do students spontaneously produce CCL across biology topics? 2) Do students use different kinds of CCL to explain different topics in biology? and 3) Is the spontaneous production of CCL associated with misconception agreement?

Students Spontaneously Use CCL in Explaining a Range of Biological Phenomena

We found that undergraduate students did spontaneously use CCL to explain a variety of biological concepts in response to open-ended prompts. Indeed, 63% of the students in our sample used some kind of CCL. This result supports two important conclusions. First, it suggests that these undergraduate students—from diverse universities and across levels for formal biology education—spontaneously generate CCL as part of their explanations of biological concepts, without any priming from instructors or materials. This finding accords with previous research showing that students use CCL in response to open-ended prompts (Opfer et al., 2012; Federer et al., 2016; Richard et al. 2017; Zhao and Schuchardt, 2019; Hartelt and Martens, 2024) and supports the hypothesis that CCL expresses intuitive ways of thinking that are easily accessible to students, expanding our previous work (Coley and Tanner, 2015). Second, this result shows that students use CCL to explain many different biological concepts present in our study, including about DNA, ecosystems, cellular structure, plant growth, and evolution. This extends previous work examining CCL in response to an open prompt about a single topic (i.e., evolution, Opfer et al., 2012; Federer et al., 2016; Hartelt and Martens, 2024; antibiotic resistance; Richard et al., 2017) and shows the widespread presence of construal-consistent thinking and language across topics in biology education.

CCL Use Varies by Topic

To better understand student use of CCL across topics, we examined how CCL varied by module in our study, with each module examining a different area of biology. Overall, we found that the type of CCL use varied markedly by biology topic. Some of these effects were in line with previous research or were predictable from how these biological concepts are generally taught, while other results were more surprising.

The findings that students’ use of CCL differed by module argues against a one-size-fits-all approach to teaching concepts—and refuting construal-consistent misconceptions—in biology. The type of intuitive understandings students deploy seems to depend on context. For example, our own data and research from other labs (e.g., Kelemen, 1999; 2012; Kampourakis and Zogza, 2008; Hammann and Nehm, 2020) show that students frequently use teleological reasoning when describing evolution, in our study more than any other construal. Thus, it may be helpful to tailor a class centered on evolution in a way that is especially mindful of addressing teleological misconceptions, or carefully avoiding teleological language. Indeed, recent work has shown that such misconceptions can be addressed (Pickett et al., 2022; Hartelt and Martens, 2024) and avoided, even in young children (e.g., Brown et al., 2020). Meanwhile, we found that anthropic language was more common when explaining other topics such as DNA and ecosystems. Given the relationship between anthropocentric language use and misconception agreement, this suggests that instructors should be particularly mindful of students’ thinking about humans when teaching these topics. Finally, we also found that essentialist and teleological language was frequently used by students explaining plant biology (i.e., growth, cell differentiation). Given that plants are often discussed in aggregate in general biology classes, potentially supporting essentialist assumptions, this could suggest a need for instructors to draw attention to differences between plants (e.g., compare and contrast different species of trees or ferns), rather than focusing on similarities among plants or differences between plants and animals. Overall, our findings show that instructors should be attentive to CCL in a wide variety of biology topics and principles, especially how this type of language may undergird misconceptions.

Spontaneous Use of Anthropocentric but Not Teleological or Essentialist CCL in Explanations of Biological Phenomena Predicts Misconception Agreement

An important concern in our work has been the relationship between cognitive construals and misconceptions about biological concepts. We have argued that intuitive ways of knowing might be useful in the context of everyday cognition but might represent obstacles to learning some formal life science concepts (Coley and Tanner, 2012; 2015). And our work as well as others have borne out this connection (e.g., Coley and Tanner, 2015; Coley et al., 2017; Richard et al., 2017; Stern et al., 2018). An alternative explanation proposed for these findings is that CCL was observed in student explanations because they were responding to misconception challenge statements that themselves contained CCL (Gouvea and Simon, 2018; Slominski et al., 2023). In this study, we found that students’ spontaneous use of CCL in response to open-ended prompts that did not contain CCL was related to their agreement with an independent measure of construal-consistent misconceptions. This finding held whether we used the mean misconception agreement or total number of statements that students agreed with and was evident when we controlled for institution and level of formal biology education. This demonstrates a robust connection between CCL use and endorsement of misconceptions and suggests that priming cannot explain our previous results.

However, the current findings also differ from our previous results. Specifically, in past work we have found selective predictive relations between types of CCL and agreement with construal-consistent misconception statements. For example, Coley and Tanner (2015) found that use of teleological language predicted agreement with teleological misconceptions, but not essentialist or anthropic misconceptions. However, in the current study the relationship between CCL use and misconception agreement was driven by students’ use of anthropocentric language. That is, students who used anthropocentric language in their open prompt responses had significantly greater agreement with misconception statements compared with students who did not use this type of language. In contrast, other types of CCL (i.e., essentialist, teleological, or anthropomorphic) did not show the same relationship with misconception agreement. This suggests something particularly problematic about anthropocentric language use in terms of student understanding of biological concepts.

What might drive the relationship between anthropocentric explanations and misconception agreement? On the one hand, using anthropocentric language might be an outcome of a poor understanding of biological concepts in general. However, when we included both anthropocentric language and biological expertise as predictors of misconception agreement, we found that anthropocentrism remained a significant predictor. This suggests that the relationship between anthropocentric language use and misconception agreement is not fully explained by a lack of biological knowledge. Another explanation could be that anthropocentrism, especially separating humans from other animals, is not biologically accurate and so is related to more misunderstandings, whereas essentialism, teleology, and anthropomorphism might have more positive benefits (see Evans and Rosengren, 2018). For example, when students solely think about ecosystems in anthropocentric terms, they may misunderstand the relationships between humans and other living things (e.g., that we are a part of, not apart from, natural ecosystems). Previous research in our lab has found that students who endorsed a larger separation between humans and nature also thought that humans were less likely to be impacted by climate change, a view that contradicts scientific consensus (Betz and Coley, 2022). In fact, recent research in ecological education has emphasized the importance of teaching students about “Social-Ecological Systems,” ecosystems that explicitly include humans, in recognition of the fact that without this emphasis Western students default to a scientifically inaccurate, anthropocentric view of ecosystems (Scyphers et al., 2015; Kim et al., 2023). The present research suggests that helping students collapse the distance between humans and other organisms is also beneficial for other biological sciences besides ecology, such as genetics, evolution, and even botany. However, further research is needed to determine the mechanism by which anthropocentric thinking influences misconceptions, particularly across biological concepts.

These results have implications for the role of cognitive construals, and anthropic language specifically, in biology teaching. There has been much debate within the science education community as to whether cognitive construals are helpful or harmful to students’ understanding (see Zohar and Ginossar, 1998; Evans and Rosengren, 2018 for examples). In our own work, we have found that university instructors frequently employ CCL in the classroom, presumably with the goal to help their students understand concepts (Betz et al., 2019). Our findings in this paper show mixed findings for how CCL might impact students’ understanding. On the one hand, we did not find a significant relationship between teleological or essentialist language use and agreement with misconceptions. This suggests that using teleology or essentialism to understand biology concepts does not necessarily lead to misconceptions and can even help students scaffold a foundation for scientific understanding (for a similar argument, see Evans and Rosengren, 2018).

Our findings also suggest that anthropic thinking has a multifaceted effect on understanding—while anthropocentric thinking was associated with greater misconception agreement, anthropomorphic thinking appeared neutral. Moreover, in previous work (Betz et al., 2019), we find that across different universities, different instructors, and different levels of biology classes, anthropic language was the most commonly observed type of construal-consistent instructor language. This suggests that biology instructors may need to be wary about describing or implying that humans are separate and superior to other species in explaining biological concepts. However, anthropomorphising nonhuman animals, organisms, and biological processes appear relatively harmless in our findings. This adds to a literature showing mixed evidence on anthropomorphism, which can increase interest in and connection with science topics (Byrne et al., 2009; Hight et al., 2021; McGellin et al., 2021) but also undermine scientific thinking (Legare et al., 2013).

Although anthropocentric thinking may have harmful impacts on students’ understanding, biology instructors can also intervene on students’ anthropocentric misconceptions. Previous research has shown that anthropocentric thinking is not necessarily a cognitive default that is present in early childhood (Herrmann et al., 2010) and that it varies by culture (Ross et al., 2003; Waxman et al., 2007) and level of education (Coley et al., 2017). This suggests that anthropocentric thinking can be impacted by how instructors teach and may be less persistent than other types of intuitive thinking. Indeed, in our own recent work, we found that students who read a text that directly refuted and corrected anthropocentric misconceptions about antibiotic resistance tended to agree less strongly with misconceptions compared with a control group of students (Nielson, 2023).

Limitations

The measures used in the study were designed to measure negative outcomes of construal consistent language, through the endorsement of misconceptions. It is entirely possible that construals can and do lead to positive outcomes, such as improved memory (see Nielson, 2023). Future studies should assess both the positive and negative consequences of CCL. We also acknowledge differences in recruitment and data collection across the two universities in our sample; however, our results remained significant when controlling for university.

We showed effects across diverse core biological concepts, strengthening our claims about the role of anthropocentric language in misconception agreement; however, such results may not fully generalize to other topics in biology or other instantiation of the core concepts we tested. Indeed, our own results suggest that CCL may be highly variable depending on specific content. Nevertheless, CCL of some kind was present in all modules. While we cannot definitively say which type of language students will use to explain a given biology topic—or to what degree—given our results, it is likely that CCL will play a role.

CONCLUSION

Undergraduate biology education is a crucial time for instruction that addresses students’ preconceived misconceptions and prepares them for successful careers and engaged citizenship. In this study, we found that U.S.-based undergraduate students regularly and spontaneously used CCL to reason about a variety of biological concepts and that CCL use, especially anthropocentric language, was related to endorsement of misconceptions. Although intuitive thinking can be persistent, anthropocentrism has been shown to be modifiable by education and direct intervention. This suggests that concerned biology educators can identify and intervene on anthropocentric thinking in their classrooms, improving students’ understanding and preparedness to engage in vital conversations about biology, climate crisis, public health, and beyond.

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