Loading [MathJax]/jax/output/SVG/jax.js
Research article Special Issues

Pre-service teachers' technology acceptance of artificial intelligence (AI) applications in education


  • We verified a pre-service teachers' Extended Technology Acceptance Model (ETAM) for AI application use in education. Partial least squares structural equation modeling (PLS-SEM) examined data from 400 pre-service teachers in Central Visayas, Philippines. Perceived usefulness and attitudes, usefulness and attitudes, ease of use and attitudes, and intention to use AI apps were significantly correlated. However, subjective norms, experience, and voluntariness did not affect how valuable AI was viewed or intended to be used. Attitudes toward AI mediated specific correlations use. These findings improve the ETAM model and highlight the significance of user-friendly AI interfaces, educational activities highlighting AI's benefits, and institutional support to enhance pre-service teachers' adoption of AI applications in education. Despite its limitations, this study establishes the foundation for further research on AI adoption in educational settings.

    Citation: Isidro Max V. Alejandro, Joje Mar P. Sanchez, Gino G. Sumalinog, Janet A. Mananay, Charess E. Goles, Chery B. Fernandez. Pre-service teachers' technology acceptance of artificial intelligence (AI) applications in education[J]. STEM Education, 2024, 4(4): 445-465. doi: 10.3934/steme.2024024

    Related Papers:

    [1] K. Kefi, Jian Liu . Triple solutions for a Leray-Lions p(x)-biharmonic operator involving Hardy potential and indefinite weight. AIMS Mathematics, 2024, 9(8): 22697-22711. doi: 10.3934/math.20241106
    [2] Jia Li, Changchun Bi . Study of weak solutions of variational inequality systems with degenerate parabolic operators and quasilinear terms arising Americian option pricing problems. AIMS Mathematics, 2022, 7(11): 19758-19769. doi: 10.3934/math.20221083
    [3] Lulu Tao, Rui He, Sihua Liang, Rui Niu . Existence and multiplicity of solutions for critical Choquard-Kirchhoff type equations with variable growth. AIMS Mathematics, 2023, 8(2): 3026-3048. doi: 10.3934/math.2023156
    [4] Xiaomin Wang, Zhong Bo Fang . New Fujita type results for quasilinear parabolic differential inequalities with gradient dissipation terms. AIMS Mathematics, 2021, 6(10): 11482-11493. doi: 10.3934/math.2021665
    [5] Khaled Kefi, Mohammed M. Al-Shomrani . On multiple solutions for an elliptic problem involving Leray–Lions operator, Hardy potential and indefinite weight with mixed boundary conditions. AIMS Mathematics, 2025, 10(3): 5444-5455. doi: 10.3934/math.2025251
    [6] José L. Díaz . Non-Lipschitz heterogeneous reaction with a p-Laplacian operator. AIMS Mathematics, 2022, 7(3): 3395-3417. doi: 10.3934/math.2022189
    [7] Jia Li, Zhipeng Tong . Local Hölder continuity of inverse variation-inequality problem constructed by non-Newtonian polytropic operators in finance. AIMS Mathematics, 2023, 8(12): 28753-28765. doi: 10.3934/math.20231472
    [8] Shulin Zhang . Existence of nontrivial positive solutions for generalized quasilinear elliptic equations with critical exponent. AIMS Mathematics, 2022, 7(6): 9748-9766. doi: 10.3934/math.2022543
    [9] Huashui Zhan, Yuan Zhi, Xiaohua Niu . On a non-Newtonian fluid type equation with variable diffusion coefficient. AIMS Mathematics, 2022, 7(10): 17747-17766. doi: 10.3934/math.2022977
    [10] Dengming Liu, Luo Yang . Extinction behavior for a parabolic p-Laplacian equation with gradient source and singular potential. AIMS Mathematics, 2022, 7(1): 915-924. doi: 10.3934/math.2022054
  • We verified a pre-service teachers' Extended Technology Acceptance Model (ETAM) for AI application use in education. Partial least squares structural equation modeling (PLS-SEM) examined data from 400 pre-service teachers in Central Visayas, Philippines. Perceived usefulness and attitudes, usefulness and attitudes, ease of use and attitudes, and intention to use AI apps were significantly correlated. However, subjective norms, experience, and voluntariness did not affect how valuable AI was viewed or intended to be used. Attitudes toward AI mediated specific correlations use. These findings improve the ETAM model and highlight the significance of user-friendly AI interfaces, educational activities highlighting AI's benefits, and institutional support to enhance pre-service teachers' adoption of AI applications in education. Despite its limitations, this study establishes the foundation for further research on AI adoption in educational settings.



    The presence of singularities and degeneracies in elliptic equations introduces significant challenges in analyzing the behavior of solutions. These singularities, especially near the origin or boundary, can profoundly affect the properties of the operator, making solutions more sensitive to changes in the domain. For instance, when 1<p<N, it is known that ˜u/|y|Lp(RN) if ˜uW1,p(RN), or ˜u/|y|Lp(Ω) when ˜uW1,p(Ω), where Ω is a bounded domain (see Lemma 2.1 in [12] for further details). In this context, the solution under consideration is ˜u, and such behavior leads to the development of Hardy-type inequalities, which are crucial for controlling the singularities of solutions near critical points, particularly when the equation includes singular potential terms (see, e.g., [1,12,17,18,20]).

    Furthermore, the presence of an indefinite weight in the source term creates several challenges, mainly because it can change sign or behave irregularly. This complicates the application of standard methods for proving the existence of solutions, such as ensuring the necessary properties of the energy functional. The irregular behavior of the weight also makes it difficult to use common mathematical tools like Sobolev embeddings and variational methods. To overcome these difficulties, this manuscript employs a more flexible approach based on critical point theory [4], which allows establishing the existence of solutions despite the complexities introduced by the indefinite weight.

    Finally, the degeneracy of differential operators, such as p-Laplacian or p(x)-Laplacian, when coupled with a weight function ω(x) inside the divergence, introduces additional complexity to the problem. The presence of ω(x), whether it is singular or merely bounded, requires a shift in the selection of appropriate functional spaces. Traditional Sobolev spaces like W1,p(Ω) or W1,p(x)(Ω) may no longer be adequate in such cases. To properly handle the singularities or degeneracies, it becomes necessary to consider alternative Sobolev spaces, such as W1,p(x)(ω,Ω) (see section 2 for the definition of W1,p(ω,Ω)), which are specifically designed to accommodate the weight function (see [6] for further details). The most recent contribution to the study of the p Laplacian in a bounded domain and in the whole space can be found in respectively in [5] and [3], furthermore, the degenerate p-Laplacian operator combined with a Hardy potential can be found in [16].

    This paper tackles the challenges posed by degeneracy, Hardy-type singularities, and sign-changing source terms, which are common in applied mathematical models, by examining a class of weighted quasilinear elliptic Dirichlet problem involving a variable exponent p(x) and an indefinite source term. The main objective is to prove the existence of three weak solutions, using a critical point theorem introduced by Bonanno and Moranno in [4] while accounting for the complexities introduced by the operator's degeneracy and the singularities in the equation.

    This manuscript explores the multiplicity of weak solutions to a weighted elliptic equations of the form:

    {Δp(x),a(x,u)u+b(x)|u|q2u|x|q=λk(x)|u|s(x)2uin Ω,u=0on Ω, (1.1)

    where λ is a positive parameter, 1<q<N, and ΩRN (with N2) is a bounded open subset with smooth boundary Ω. The function u is a solution to a weighted quasilinear elliptic equation involving a variable exponent p(x)C+(¯Ω)(see, the beginning of Section 2) and the nonlinear source term of the form k(x)|u|s(x)2u which involves a weight function k(x) and may exhibit singularities on Ω and can change sign, belongs to a nonstandard Lebesgue space Lγ(x)(Ω).

    The operator Δp(x),a(x,u)u represents a nonlinear generalization of the classical Laplacian, defined by:

    Δp(x),a(x,u)u=div(a(x,u)|u|p(x)2u),

    here a(x,u) denotes a Carathéodory function satisfying the inequality:

    a1ω(x)a(x,u)a2ω(x),

    with a1,a2 are two positive constants, the function ω(x) is assumed to belongs to the local Lebesgue space L1loc(Ω), and it satisfies additional growth conditions, such as ωh(x)L1(Ω), where h(x) satisfies certain bounds related to the variable exponent p(x). Specifically, we assume that

    (ω)ωh(x)L1(Ω),forh(x)C(¯Ω)andh(x)(Np(x),+)[1p(x)1,+).

    The nonlinearity in the equation involves the functions k(x) and s(x), which are assumed to satisfy the following inequality for almost every xΩ

    (k)1<s(x)<ph(x)<N<γ(x),

    where ph(x)=h(x)p(x)h(x)+1.

    Set, S(Ω), the space that contains all measurable functions in Ω and

    C+(¯Ω)={p(x)|p(x)C(¯Ω), p(x)>1, x¯Ω},
    p+=maxx¯Ωp(x),p=minx¯Ωp(x).

    For τ>0, and p(x)C+(¯Ω), we use the following notations

    τˆp=max{τp, τp+},  τˇp=min{τp, τp+}.

    In the sequel, we define the space Lp(x)(ω,Ω) as follows

    Lp(x)(ω,Ω)={uS(Ω)Ωω(x)|u(x)|p(x)dx<},

    where p(x) is a variable exponent, and ω(x) is a weight function. The space is endowed with a Luxemburg-type norm, given by:

    uLp(x)(ω,Ω)=inf{ν>0Ωω(x)|u(x)ν|p(x)dx1}.

    Next, we define the corresponding variable exponent Sobolev space, which incorporates the variable exponent p(x) in the functional setting.

    W1,p(x)(Ω)={uLp(x)(Ω): |u|Lp(x)(Ω)},

    with the norm

    uW1,p(x)(Ω)=up(x)+up(x),

    where up(x)=|u|p(x),|u|=(Ni=1|uxi|2)12,u=(ux1,ux2,...,uxN) is the gradient of u at (x1,x2,...,xN).

    Denote, by

    W1,p(x)(ω,Ω)={uLp(x)(Ω):ω1p(x)|u|Lp(x)(Ω)}

    the weighted Sobolev space and by W1,p(x)0(ω,Ω) as the closure of C0(Ω) in the space W1,p(x)(ω,Ω) endowed with the norm

    u=inf{ν>0:Ωω(x)|u(x)ν|p(x)dx1}.

    Lemma 2.1. [8] If p1(x),p2(x)C+(¯Ω) such that p1(x)p2(x) a.e. xΩ, then there exists the continuous embedding W1,p2(x)(Ω)W1,p1(x)(Ω).

    Proposition 2.1 ([9]) For p(x)C+(¯Ω),u,unLp(x)(Ω), one has

    min{upp(x),up+p(x)}Ω|u(x)|p(x)dxmax{upp(x),up+p(x) }.

    Let 0<d(x)S(Ω), and define the space

    Lp(x)(d,Ω):=Lp(x)d(x)(Ω)={uS(Ω)Ωd(x)|u(x)|p(x)dx<},

    where p(x) is a variable exponent, and d(x) is a weight function. The space is equipped with a Luxemburg-type norm, defined by

    uLp(x)d(x)(Ω)=u(p(x),d(x)):=inf{ν>0Ωd(x)|u(x)ν|p(x)dx1}.

    Proposition 2.2 ([10]) If pC+(¯Ω). Then

    min{up(p(x),d(x)),up+(p(x),d(x))}Ωd(x)|u(x)|p(x)dxmax{up(p(x),d(x)),up+(p(x),d(x))}

    for every uLp(x)d(x)(Ω) and for a.e. xΩ.

    Combining Proposition 2.1 with Proposition 2.2, one has

    Lemma 2.2. Let

    ρω(u)=Ωω(x)|u(x)|p(x)dx.

    For pC+(¯Ω),uW1,p(x)(ω,Ω), we have

    min{up,up+}ρω(u)max{up,up+}.

    From Proposition 2.4 of [20], if (ω) holds, W1,p(x)(ω,Ω) is a reflexive separable Banach space.

    From Theorem 2.11 of [15], if (ω) holds, the following embedding

    W1,p(x)(ω,Ω)W1,ph(x)(Ω) (2.1)

    is continuous, where

    ph(x)=p(x)h(x)h(x)+1<p(x).

    Combining (2.1) with Proposition 2.7 and Proposition 2.8 in [11], we get the following embedding

    W1,p(x)(ω,Ω)Lr(x)(Ω)

    is continuous, where

     1r(x)ph(x)=Nph(x)Nph(x)=Np(x)h(x)Nh(x)+Np(x)h(x).

    Furthermore, the following embedding

    W1,p(x)(ω,Ω)↪↪Lt(x)(Ω)

    is compact, when 1t(x)<ph(x).

    In what follows, and for any p(x)C+(¯Ω), let us denote by p(x):=p(x)p(x)1, the conjugate exponent of p(x).

    Remark 2.1. Under Condition (k), one has

    1<β(x)<ph(x) for almost every xΩ, where β(x):=γ(x)s(x)γ(x)s(x), consequently

    W1,p(x)(ω,Ω)↪↪Lβ(x)(Ω)

    is compact.

    1<α(x)<ph(x) for almost every xΩ, where α(x)=γ(x)s(x), consequently

    W1,p(x)(ω,Ω)↪↪Lα(x)(Ω)

    is compact.

    Lemma 2.3 (Hölder type inequality [2,11]). Let p1,p2,t1 three functions that belong in S(Ω) such that

    1t(x)=1p1(x)+1p2(x),for almost every xΩ.

    If fLp1(x)(Ω) and gLp2(x)(Ω), then fgLt(x)(Ω), moreover

    fgt(x)2fp1(x)gp2(x).

    Similarly, if 1t(x)+1p1(x)+1p2(x)=1, for a.e. xΩ, then

    Ω|f(x)g(x)h(x)|dx3ft(x)gp1(x)hp2(x).

    Lemma 2.4 ([7]). Let r1(x) and r2(x) be measurable functions such that r1(x)L(Ω), and 1r1(x)r2(x), for a.e. xΩ. Let wLr2(x)(Ω), w0. Then

    wˇr1r1(x)r2(x)|w|p(x)r2(x)wˆpr1(x)r2(x).

    Let's define the functional Iλ:W1,p(x)0(ω,Ω)R as

    Iλ(u):=L(u)λM(u),

    where

    L(u):=Ωa(x,u)p(x)|u|p(x)dx+1qΩb(x)|u|q|x|qdx, (2.2)

    and

    M(u):=Ω1s(x)k(x)|u|s(x)dx. (2.3)

    It is noted that, based on Remark 2.1 and Lemma 2.4, the aforementioned functionals are both well-defined and continuously Gâteaux differentiable (see [14] for further details). The Gâteaux derivatives are as follows

    L(u),v=Ωa(x,u)|u|p(x)2uvdx+Ωb(x)|u|q2uv|x|qdx,

    and

    M(u),v=Ωk(x)|u|s(x)2uvdx.

    Furthermore, M(u) is compact in the dual space (W1,p(x)0(ω,Ω)) (see [14]).

    uW1,p(x)0(ω,Ω) is said to be a weak solution of the problem (1.1) if, the following holds for every vW1,p(x)0(ω,Ω).

    Iλ(u),v=L(u),vλM(u),v=0.

    Lemma 2.5. L is a strictly monotone coercive functional that belongs in (W1,p(x)0(ω,Ω)).

    Proof. For any uW1,p(x)0(ω,Ω){0}, by Lemma 2.2, one has

    L(u)(u)=Ωa(x,u)|u|p(x)2uudx+Ωb(x)|u|q2u2|x|qdxa1ρω(u)a1min{up+,up},

    thus

    limuL(u)(u)ua1limumin{up+,up}u=+,

    then L is coercive in view of p(x)C+(¯Ω).

    According to (2.2) of [13], for all x,yRN, there is a positive constant Cp such that

    |x|p2x|y|p2y,xyCp|xy|p, if p2,

    and

    |x|p2x|y|p2y,xyCp|xy|2(|x|+|y|)2p, if 1<p<2, and (x,y)(0,0),

    where .,. is the usual inner product in RN. Thus, for any u,vX satisfying uv, by standard arguments we can obtain

    L(u)L(v),uv=Ωa(x,u)(|u|p(x)2u|v|p(x)2v)(uv)dx      +Ωb(x)|x|q(|u|q2u|v|q2v)(uv))dx>0,

    hence, one has L is strictly monotone in W1,p(x)0(ω,Ω).

    Lemma 2.6. The functional L is a mapping of (S+)-type, i.e. if unu in W1,p(x)0(ω,Ω), and ¯limnL(un)L(u),unu)0, then unu in W1,p(x)0(ω,Ω).

    Proof. Let unu in W1,p(x)0(ω,Ω), and ¯limnL(un)L(u),unu0.

    Noting that L is strictly monotone in W1,p(x)0(ω,Ω), we have

    limnL(un)L(u),unu=0,

    while

    L(un)L(u),unu=Ωa(x,u)(|un|p(x)2un|u|p(x)2u)(unu)dx   +Ω(b(x)|un|q2|x|qun(unu)b(x)|u|q2|x|qu(unu))dx,

    thus we get

    ¯limnΩa(x,u)(|un|p(x)2un|u|p(x)2u)(unu)dx0.

    Further, by (1.2) one has

    ¯limnΩω(x)(|un|p(x)2un|u|p(x)2u)(unu)dx0,

    then unu in W1,p(x)0(ω,Ω) via Lemma 3.2 in [19].

    Lemma 2.7. L is an homeomorphism.

    Proof. The strict monotonicity of L implies that it is injective. Since L is coercive, it is also surjective, and hence L has an inverse mapping.

    Next, we show that the inverse mapping (L)1 is continuous.

    Let ˜fn,˜f(W1,p(x)0(ω,Ω)) such that ˜fn˜f. We aim to prove that (L)1(˜fn)(L)1(˜f).

    Indeed, let (L)1(˜fn)=un and (L)1(˜f)=u, so that L(un)=˜fn and L(u)=˜f. By the coercivity of L, the sequence un is bounded. Without loss of generality, assume unu0, which implies

    limn(L(un)L(u),unu0)=limn(˜fn˜f,unu0)=0.

    Thus, unu0 because L is of (S+)-type, which ensures that L(un)L(u0). Combining this with L(un)L(u), we deduce that L(u)=L(u0). Since L is injective, it follows that u=u0, and hence unu. Therefore, we have (L)1(˜fn)(L)1(˜f), proving that (L)1 is continuous.

    The following critical point theorems constitute the principal tools used to obtain our result.

    Theorem 2.1. ([4, Theorem 3.6]). Let X be a reflexive real Banach space and assume the following

    L:XR be a coercive functional that is continuously Gateaux differentiable and weakly lower semicontinuous in the sequential sense

    The Gateaux derivative of L has a continuous inverse on the dual space X.

    M:XR is a continuously Gateaux differentiable functional whith a compact Gateaux derivative.

    Furthermore, suppose that

    (a0)infXL=L(0)=0 and M(0)=0.

    There exist a positive constant d and a point ¯vX such that d06L(¯v), and the following conditions are satisfied:

    (a1)supL(x)<dM(x)d<M(¯v)L(¯v),
    (a2)For each λΛd:=(L(¯v)M(¯v),dsupL(x)dM(x)),the functional Iλ:=LλM is coercive.

    Then, for any λΛd, LλM has at least three distinct critical points in X.

    In this section, a theorem about the existence of at least three weak solutions to the problem (1.1) is obtained.

    Recall the Hardy inequality (refer to Lemma 2.1 in [12] for more details), which asserts that for 1<t<N, the following inequality holds:

    Ω|u(x)|t|x|tdx1HΩ|u|tdx,uW1,t0(Ω),

    where the optimal constant H is given by:

    H=(Ntt)t.

    By combining this with Lemma 2.1 and using the fact that 1<q<ph(x)<N, we deduce the continuous embeddings

    W1,p(x)0(ω,Ω)W1,ph(x)0(Ω)W1,q0(Ω),

    which leads to the inequality

    Ω|u(x)|q|x|qdx1HΩ|u|qdx,uW1,p(x)0(ω,Ω),

    where H=(Nqq)q.

    We are now ready to present our primary result. To this end, we define

    ˜D(x):=sup{˜D>0B(x,˜D)Ω}

    for each xΩ, here B(x,˜D) denotes a ball centered at x with radius ˜D. It is clear that there exists a point x0Ω such that B(x0,R)Ω, where

    R=supxΩ˜D(x).

    In the remainder, assume that k(x), fulfill this requirement

    k(x):={0, forxΩB(x0,R),k0, forxB(x0,R2),>0, for xB(x0,R)B(x0,R2),

    where k0 is a positive constant, the symbol ˜m will represent the constant

    ˜m=πN2N2Γ(N2),

    with Γ denoting the Gamma function.

    Theorem 3.1. Assume that p>s+, and, there exist two positive constants d and δ>0, such that

    1p+(2δR)ˇpwL1(B)=d,

    and

    Aδ:=1p(2δR)ˆpωL1(B)+(2δR)qbqH˜m(RN(R2)N)1s+k0δˇs˜m(R2)N<Bd:=dcˆsγskγ(x)s[(p+d)1ˇp]ˆs,

    then for any λ]Aδ,Bd[, problem (1.1) has at least three weak solutions.

    Proof. It is worth noting that the functional L and M associated with problem (1.1) and defined in (2.2) and (2.3), satisfy the regularity assumptions outlined in Theorem 2.1. We will now establish the fulfillment of conditions (a1) and (a2). To this end, let's consider

    1p+(2δR)ˇpωL1(B)=d

    and consider vdX such that

    vδ(x):={0xΩB(x0,R)2δR(R|xx0|)xB:=¯B(x0,R)B(x0,R2),δx¯B(x0,R2).

    Then, by the definition of L, we have

    1p+(2δR)ˇpωL1(B)<L(vδ)1p(2δR)ˆpωL1(B)+(2δR)qbqH˜m(RN(R2)N)

    Therefore, L(vδ)>d. However, it is important to consider the following

    M(vδ)B(x0,R2)k(x)s(x)|vδ|γ(x)dx1s+k0δˇs˜m(R2)N (3.1)

    In addition, for each uL1(],d]), we have

    1p+uˇpd. (3.2)

    therefore,

    u(p+L(u))1ˇp<(p+d)1ˇp.

    Furthermore, we can deduce using Lemmas 2.3, 2.4 and Remark 2.1 the following

    M(u)1skγ(x)|u|s(x)γ(x)1sks(x)(cγsu)ˆs, (3.3)

    where cγs is the constant from the continuous embedding of W1,p(x)0(ω,Ω) into W1,γ(x)s(x)(Ω).

    This leads to the following result

    supL(u)<dM(u)cˆsγskγ(x)s[(p+d)1ˇp]ˆs,

    and

    1dsupL(u)<dM(u)<1λ.

    Furthermore, we can establish the coerciveness of Iλ for any positive value of λ by employing inequality (3.1) once more. This yields the following result

    M(u)cˆsγskγ(x)suˆs.

    When u is great enough, the following can be inferred

    L(u)λM(u)1p+upλcˆsγskγ(x)suˆs.

    By considering the fact that p>s+, we can reach the desired conclusion. In conclusion, considering the aforementioned fact that

    ˉΛd:=(Aδ,Bd)(L(vδ)M(vδ),dsupL(u)<dM(u)),

    since all assumptions of Theorem 2.1 are fulfilled, it can be deduced that for any λˉΛd, the function LλM possesses at least three critical points that belong in X:=W1,p0(ω,Ω). Consequently these critical points are exactly weak solutions of problem (1.1).

    Khaled Kefi: Conceptualization, Methodology, Writing–original draft, Supervision; Nasser S. Albalawi: Conceptualization, Methodology, Writing–original draft, Supervision. All authors have read and agreed to the published version of the manuscript.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    The authors extend their appreciation to the Deanship of Scientific Research at Northern Border University, Arar, KSA for funding this research work through the project number NBU-FPEJ-2025-1706-01.

    The authors declare that they have no conflicts of interest.



    [1] Bozkurt, A., Karadeniz, A., Baneres, D., Guerrero-Roldan, A.E. and Rodriguez, M.E., Artificial intelligence and reflections from educational landscape: A review of AI studies in half a century. Sustainability, 2021, 13(2): 800. https://doi.org/10.3390/su13020800 doi: 10.3390/su13020800
    [2] Chiu, T.K.F., Meng, H., Chai, C.S., King, I., Wong, S. and Yeung, Y., Creation and evaluation of a pre-tertiary Artificial Intelligence (AI) curriculum. IEEE Transactions on Education, 2021, 65(1): 30-39. https://doi.org/10.1109/TE.2021.3085878 doi: 10.1109/TE.2021.3085878
    [3] Holmes, W., Hui, Z., Miao, F. and Ronghuai, H., AI and education: A guidance for policymakers, 2021, UNESCO Publishing.
    [4] Alghamdi, A.A., Alanezi, M.A. and Khan, Z.F., Design and implementation of a computer aided intelligent examination system. International Journal of Emerging Technologoies in Learning, 2020, 15(1): 30-44. https://doi.org/10.3991/ijet.v15i01.11102 doi: 10.3991/ijet.v15i01.11102
    [5] Cao, J.J., Yang, T., Lai, I.K.W. and Wu, J., Student acceptance of intelligent tutoring systems during COVID-19: The effect of political influence. International Journal of Electrical Engineering Education, 2023, 60(1_suppl): 2495-2509. https://doi.org/10.1177/00207209211003270 doi: 10.1177/00207209211003270
    [6] Kong, J.S.M., Teo, B.S., Lee, Y.J., Pabba, A.B., Lee, E.J.D. and Sng, J.C.G., Virtual integrated patient: An AI supplementary tool for second-year medical students. Asia Pacific Scholar, 2021, 6(3): 87. https://doi.org/10.29060/TAPS.2021-6-3/SC2394 doi: 10.29060/TAPS.2021-6-3/SC2394
    [7] Aldosari, S.A.M., The future of higher education in the light of artificial intelligence transformations. International Journal of Higher Education, 2020, 9(3): 145-151. https://doi.org/10.5430/ijhe.v9n3p145 doi: 10.5430/ijhe.v9n3p145
    [8] Bellod, H.C., Ramón, V.B., Fernández, E.C. and Luján, J.F.G., Analysis of stress and academic-sports commitment through self-organizing artificial neural networks. Challenges, 2021, 42: 136-144. https://doi.org/10.47197/RETOS.V42I0.86983 doi: 10.47197/RETOS.V42I0.86983
    [9] Banerjee, M., Chiew, D., Patel, K.T., Johns, I., Chappell, D., Linton, N., et al., The impact of artificial intelligence on clinical education: Perceptions of postgraduate trainee doctors in London (UK) and recommendations for trainers. BMC Medical Education, 2021, 21: 1-10. https://doi.org/10.1186/s12909-021-02870-x doi: 10.1186/s12909-021-02870-x
    [10] Bates, T., Cobo, C., Mariño, O. and Wheeler, S., Can artificial intelligence transform higher education? International Journal of Educational Technology in Higher Education, 2020, 17: 1-12. https://doi.org/10.1186/s41239-020-00218-x doi: 10.1186/s41239-020-00218-x
    [11] Bonneton-Botte, N., Fleury, S., Girard, N., Le Magadou, M., Cherbonnier, A., Renault, M., et al., Can tablet apps support the learning of handwriting? An investigation of learning outcomes in kindergarten classroom. Computers & Education, 2020,151: 103831. https://doi.org/10.1016/j.compedu.2020.103831 doi: 10.1016/j.compedu.2020.103831
    [12] Bennane, A., Adaptive educational software by applying reinforcement learning. Informatics in Education-An International Journal, 2013, 12(1): 13–27.
    [13] González-Calatayud, V., Prendes-Espinosa, P. and Roig-Vila, R., Artificial intelligence for student assessment: A systematic review. Applied Sciences, 2021, 11(12): 5467. https://doi.org/10.3390/app11125467 doi: 10.3390/app11125467
    [14] Garg, S. and Sharma, S., Impact of artificial intelligence in special need education to promote inclusive pedagogy. International Journal of Information and Education Technology, 2020, 10(7): 523-527. https://doi.org/10.18178/ijiet.2020.10.7.1418 doi: 10.18178/ijiet.2020.10.7.1418
    [15] Kim, H.S., Kim, N.Y. and Cha, Y., Is it beneficial to use AI chatbots to improve learners' speaking performance? Journal of ASIA TEFL, 2021, 18(1): 161-178. https://doi.org/10.18823/asiatefl.2021.18.1.10.161 doi: 10.18823/asiatefl.2021.18.1.10.161
    [16] Luckin, R., Towards artificial intelligence-based assessment systems. Nature Human Behaviour, 2017, 1(3): 0028. https://doi.org/10.1038/s41562-016-0028 doi: 10.1038/s41562-016-0028
    [17] Haseski, H.I., What do Turkish pre-service teachers think about artificial intelligence? International Journal of Computer Sciences and Engineering Systems, 2019, 3(2): 3-23. https://doi.org/10.21585/ijcses.v3i2.55 doi: 10.21585/ijcses.v3i2.55
    [18] Hill, J., Randolph Ford, W. and Farreras, I.G., Real conversations with artificial intelligence: A comparison between human–human online conversations and human–chatbot conversations. Computers in Human Behavior, 2015, 49: 245-250. https://doi.org/10.1016/j.chb.2015.02.026 doi: 10.1016/j.chb.2015.02.026
    [19] Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 1989,319-340. https://doi.org/10.2307/249008 doi: 10.2307/249008
    [20] Davis, F.D., Bagozzi, R.P. and Warshaw, P.R., Extrinsic and Intrinsic Motivation to Use Computers in the Workplace1. Journal of Applied Social Psychology, 1992, 22(14): 1111-1132. https://doi.org/10.1111/j.1559-1816.1992.tb00945.x doi: 10.1111/j.1559-1816.1992.tb00945.x
    [21] Ajzen, I., The theory of planned behaviour: Reactions and reflections. Psychology & Health, 2011, 26(9): 1113-1127. https://doi.org/10.1080/08870446.2011.613995 doi: 10.1080/08870446.2011.613995
    [22] Venkatesh, V. and Davis, F.D., A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 2000, 46(2): 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926 doi: 10.1287/mnsc.46.2.186.11926
    [23] Díaz, B. and Nussbaum, M., Artificial intelligence for teaching and learning in schools: the need for pedagogical intelligence. Computers & Education, 2024, 105071. https://doi.org/10.1016/j.compedu.2024.105071 doi: 10.1016/j.compedu.2024.105071
    [24] Rahm, L. and Rahm-Skågeby, J., Imaginaries and problematisations: A heuristic lens in the age of artificial intelligence in education, British Journal of Educational Technology, 2023, 54(5): 1147-1159. https://doi.org/10.1111/bjet.13319 doi: 10.1111/bjet.13319
    [25] Rogers, E.M., Diffusion of innovations (5th ed.), 2023, New York, Free Press.
    [26] Venkatesh, V., Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 2000, 11(4): 342-365. https://psycnet.apa.org/doi/10.1287/isre.11.4.342.11872
    [27] Venkatesh, V. and Bala, H., Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 2008, 39(2): 273-315. https://doi.org/10.1111/j.1540-5915.2008.00192.x doi: 10.1111/j.1540-5915.2008.00192.x
    [28] Zhang, C., Schießl, J., Plößl, L. and Hofmann, F., Acceptance of artificial intelligence among pre-service teachers: A multigroup analysis. International Journal of Educational Technology in Higher Education, 2023, 20(1): 49. https://doi.org/10.1186/s41239-023-00420-7 doi: 10.1186/s41239-023-00420-7
    [29] Ofosu-Ampong, K., Acheampong, B., Kevor, M. and Amankwah-Sarfo, F., Acceptance of artificial intelligence (ChatGPT) in education: trust, innovativeness and psychological need of students. Information and Knowledge Management, 2023, 13(4): 37-47. https://doi.org/10.7176/IKM/13-4-03 doi: 10.7176/IKM/13-4-03
    [30] Al-Emran, M., Mezhuyev, V. and Kamaludin, A., Technology acceptance model in m-learning context: A systematic review. Computers & Education, 2018,125: 389-412. https://doi.org/10.1016/j.compedu.2018.06.008 doi: 10.1016/j.compedu.2018.06.008
    [31] Al Darayseh, A., Acceptance of artificial intelligence in teaching science: Science teachers' perspective. Computers and Education: Artificial Intelligence, 2022, 4: 100132. https://doi.org/10.1016/j.caeai.2023.100132 doi: 10.1016/j.caeai.2023.100132
    [32] Esiyok, E., Gokcearslan, S. and Kucukergin, K.G., Acceptance of educational use of AI chatbots in the context of self-directed learning with technology and ICT self-efficacy of undergraduate students. International Journal of Human-Computer Interaction, 2024, 1-10. https://doi.org/10.1080/10447318.2024.2303557 doi: 10.1080/10447318.2024.2303557
    [33] Lee, D. and Lehto, M., User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers & Education, 2013, 61: 193-208. https://doi.org/10.1016/j.compedu.2012.10.001 doi: 10.1016/j.compedu.2012.10.001
    [34] Chow, M., Herold, D., Choo, T. and Chan, K., Extending the technology acceptance model to explore the intention to use second life for enhancing healthcare education. Computers & Education, 2012, 59(4): 1136-1144. https://doi.org/10.1016/j.compedu.2012.05.011 doi: 10.1016/j.compedu.2012.05.011
    [35] Saqr, R.R., Abdullah, S. and Sarhan, M.Y., Exploring the Acceptance and User Satisfaction of AI-Driven e-Learning Platforms (Blackboard, Moodle, Edmodo, Coursera and edX): An Integrated Technology Model. Sustainability, 2023, 16(1): 204. https://doi.org/10.3390/su16010204 doi: 10.3390/su16010204
    [36] Chang, C., Hajiyev, J. and Su, C., Examining the students' behavioral intention to use e-learning in Azerbaijan? The general extended technology acceptance model for e-learning approach. Computers & Education, 2017,111: 128-143. https://doi.org/10.1016/j.compedu.2017.04.010 doi: 10.1016/j.compedu.2017.04.010
    [37] Nja, C.O., Idiege, K.J., Uwe, U.E., Meremikwu, A.N., Ekon, E.E., Erim, C.M., et al., Adoption of artificial intelligence in science teaching: From the vantage point of the African science teachers. Smart Learning Environments, 2023, 10(1): 42. https://doi.org/10.1186/s40561-023-00261-x doi: 10.1186/s40561-023-00261-x
    [38] Wu, W., Zhang, B., Li, S. and Liu, H., Exploring factors of the willingness to accept AI-assisted learning environments: an empirical investigation based on the UTAUT model and perceived risk theory. Frontiers in Psychology, 2022, 13: 870777. https://doi.org/10.3389/fpsyg.2022.870777 doi: 10.3389/fpsyg.2022.870777
    [39] Jhantasana, C., Intrinsic and extrinsic motivation for university staff satisfaction: confirmatory composite analysis and confirmatory factor analysis. Asia Social Issues, 2022, 15(2): 249810. https://doi.org/10.48048/asi.2022.249810 doi: 10.48048/asi.2022.249810
    [40] Ajzen, I. and Fishbein, M., Understanding attitudes and predicting social behavior, 1980, Prentice Hall, Englewood Cliffs.
    [41] Soper, D., Calculator: a-priori sample size for structural equation models. Free Statistics Calculators, 2024. https://www.danielsoper.com/statcalc/calculator.aspx?id = 89
    [42] Hair, J.F., Hult, G.T.M., Ringle, C.M. and Sarstedt, M., A primer on partial least squares equation modeling (PLS-SEM), 2017, SAGE Publications, Inc., Los Angeles.
    [43] Kim, S. and Lee, Y., Investigation into the influence of socio-cultural factors on attitudes toward artificial intelligence. Education and Information Technologies, 2024, 29(8): 9907-9935. https://doi.org/10.1007/s10639-023-12172-y doi: 10.1007/s10639-023-12172-y
    [44] Wu, R. and Yu, Z., Do AI chatbots improve students' learning outcomes? Evidence from a meta-analysis. British Journal of Educational Technology, 2024, 55(1): 10-33. https://doi.org/10.1111/bjet.13334 doi: 10.1111/bjet.13334
    [45] Chin, C., Wong, W., Cham, T., Thong, J.Z. and Ling, J., Exploring the usage intention of AI-powered devices in smart homes among millennials and millennials: the moderating role of trust. Young Consumers, 2024, 25(1): 1-27. https://doi.org/10.1108/YC-05-2023-1752 doi: 10.1108/YC-05-2023-1752
    [46] Stein, JP., Messingschlager, T. and Gnambs, T., Attitudes towards AI: measurement and associations with personality. Scientific Reports, 2024, 14(1): 2909. https://doi.org/10.1038/s41598-024-53335-2 doi: 10.1038/s41598-024-53335-2
    [47] Kaya, F., Aydin, F., Schepman, A., Rodway, P., Yetişensoy, O. and Kaya, M., The roles of personality traits, AI anxiety, and demographic factors in attitudes toward Artificial Intelligence. International Journal of Human-Computer Interaction, 2024, 40(2): 497-514. https://doi.org/10.1080/10447318.2022.2151730 doi: 10.1080/10447318.2022.2151730
    [48] Ismaila, T.S., Musa, A.A. and Adebayo, E.T., Investigating pre-service teachers' artificial intelligence perception from the perspective of planned behavior theory. Computers and Education: Artificial Intelligence, 2024, 6: 100202. https://doi.org/10.1016/j.caeai.2024.100202 doi: 10.1016/j.caeai.2024.100202
    [49] Pokrivcakova, S., Pre-service teachers' attitudes towards artificial intelligence and its integration into EFL teaching and learning. Sciendo, 2024, 11(3): 100-114. https://doi.org/10.2478/jolace-2023-0031 doi: 10.2478/jolace-2023-0031
  • Author's biography Prof. Isidro Max V. Alejandro is an Associate Professor of Professional Education, Online Administrator at the College of Teacher Education, and the previous Technical/Vocational Certification Director at Cebu Normal University, Philippines. He specializes in educational technology, biology education, and educational leadership. His research interests include technology in education, teaching-learning in science, and technology management. He is a member of the Biology Teachers Association (BIOTA), the Philippine Association for Teachers and Educators (PAFTE), and the State Universities and Colleges Teacher Educators Association (SUCTEA); Dr. Joje Mar P. Sanchez is a Professor of Science Education, the Doctorate Program Chair at the College of Teacher Education, and the former Institute for Research in Innovative Instructional Delivery Director at Cebu Normal University, Philippines. He specializes in technology and innovative strategies in science. His research interests include chemistry and physics, environmental education, advanced mixed methods, educational data mining, and science investigatory project instruction. He is a member of the National Research Council of the Philippines (NRCP), Philippine Association of Chemistry Teachers (PACT), Samahang Pisika ng Pilipinas (SPP), PAFTE, and SUCTEA; Dr. Gino G. Sumalinog is a Professor of English Language Teaching and Research Chair at the College of Teacher Education at Cebu Normal University. He specializes in technology in education and English language teaching. His research interests include English and mother tongue-based instruction studies. He is a member of the Philippine Association for Language Teaching (PALT), NRCP, PAFTE, and SUCTEA; Dr. Janet A. Mananay is an Associate Professor of English Language Teaching at the College of Teacher Education and Internationalization Director at Cebu Normal University. She specializes in technology in education and English language teaching. Her research interests include English and mother tongue-based instruction, internationalization, and global citizenship. She is a member of PAFTE and SUCTEA; Dr. Charess E. Goles is an Associate Professor of Technology and Livelihood Education and Internationalization Chair at the College of Teacher Education. She specializes in technology in education and TLE. Her research interests include food systems, especially on Cebuano local resources like seaweeds. She is a Philippine Home Economics Association (PHEA), PAFTE, and SUCTEA member; Dr. Chery B. Fernandez is an Associate Professor of Guidance and Counseling and the Guidance Counselor at the College of Teacher Education. She specializes in guidance and counseling and values education. Her research interests include counseling, peer facilitation, and group dynamics. She is a member of the Philippine Guidance and Counseling Association (PGCA), PAFTE, and SUCTEA
    Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(2949) PDF downloads(231) Cited by(1)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog